Paper Digest: NeurIPS 2022 Highlights
Download NeurIPS-2022-Paper-Digests.pdf– highlights of all NeurIPS-2022 papers. Readers can search these papers by keywords using NeurIPS-2022 Search Console. In addition, we identified ~1,000 papers that have published their code or data. Clicking on ‘related code’ link under paper title will take you directly to the codebase. To browse papers by author & read each author’s notable work review writen by our machine, here is a list of top NeurIPS-2022 authors. You may also like to explore our “Best Paper” Digest (NIPS), which lists the most influential NIPS papers in the last 30 years. The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. In 2022, it is to be held in New Orleans.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: NeurIPS 2022 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Not All Bits Have Equal Value: Heterogeneous Weight Precisions Via Trainable Noise Tensors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a method to directly optimize how many bits are used to represent each parameter in a network. |
Pedro Savarese; Xin Yuan; Yanjing Li; Michael Maire; |
| 2 | S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present three novel strategies to incorporate a parametric body model into a pixel-aligned implicit model for single-view clothed human reconstruction. |
Kennard Chan; Guosheng Lin; Haiyu Zhao; Weisi Lin; |
| 3 | Target Alignment in Truncated Kernel Ridge Regression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study how the alignment between the target function and the kernel affects the performance of the KRR. |
Arash Amini; Richard Baumgartner; Dai Feng; |
| 4 | Uncertainty Estimation Using Riemannian Model Dynamics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we combine parametric and nonparametric methods for uncertainty estimation through a novel latent space based metric. |
Guy Tennenholtz; Shie Mannor; |
| 5 | Bivariate Causal Discovery for Categorical Data Via Classification with Optimal Label Permutation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel causal model for categorical data based on a new classification model, termed classification with optimal label permutation (COLP). |
Yang Ni; |
| 6 | Adversarial Reprogramming Revisited Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show that neural networks with random weights are susceptible to adversarial reprogramming, and that in some settings training the network can cause its adversarial reprogramming to fail. |
Matthias Englert; Ranko Lazic; |
| 7 | Efficient and Effective Augmentation Strategy for Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose an effective augmentation strategy for Adversarial Training that can be integrated with several Adversarial Training algorithms and data augmentations. |
Sravanti Addepalli; Samyak Jain; Venkatesh Babu R; |
| 8 | Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Building on the pedagogy and pragmatism concepts from Developmental Psychology, we show how learning from demonstration can benefit from a Bayesian goal inference mechanism to reduce goal ambiguity and learn faster in multi-goal environments. |
Hugo Caselles-Dupré; Olivier Sigaud; Mohamed CHETOUANI; |
| 9 | Instance-based Learning for Knowledge Base Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we proposed a new method for knowledge base completion (KBC): instance-based learning (IBL). |
Wanyun Cui; Xingran Chen; |
| 10 | On The Convergence Theory for Hessian-Free Bilevel Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper provides a novel convergence rate analysis for Hessian-free bilevel algorithms with partial hypergradient estimation. |
Daouda Sow; Kaiyi Ji; Yingbin Liang; |
| 11 | Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We presented a method DiFa to address the diverse generation and faithful adaptation issues for one-shot generative domain adaption. |
Yabo Zhang; mingshuai Yao; Yuxiang Wei; Zhilong Ji; Jinfeng Bai; Wangmeng Zuo; |
| 12 | Pay Attention to Your Loss : Understanding Misconceptions About Lipschitz Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Lipschitz neural network are good classifiers: they are expressive, they are provably robust, and they generalize. |
Louis Béthune; Thibaut Boissin; Mathieu Serrurier; Franck Mamalet; Corentin Friedrich; Alberto Gonzalez Sanz; |
| 13 | Decision Trees with Short Explainable Rules Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: There is indeed a vast literature on the design and analysis of decision tree algorithms that aim at optimizing these parameters.This paper contributes to this important line of research: we propose as a novel criterion of measuring the interpretability of a decision tree, the sparsity of the set of attributes that are (on average) required to explain the classification of the examples. |
Ferdinando Cicalese; Victor Feitosa Souza; Eduardo Laber; Marco Molinaro; |
| 14 | Does Momentum Change The Implicit Regularization on Separable Data? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We derive the implicit regularization of momentum-based optimizers on the linearly separable datasets. |
Bohan Wang; Qi Meng; Huishuai Zhang; Ruoyu Sun; Wei Chen; Zhi-Ming Ma; Tie-Yan Liu; |
| 15 | Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present SHGP, a novel Self-supervised Heterogeneous Graph Pre-training approach, which does not need to generate any positive examples or negative examples. |
Yaming Yang; Ziyu Guan; Zhe Wang; Wei Zhao; Cai Xu; Weigang Lu; Jianbin Huang; |
| 16 | Object Scene Representation Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose Object Scene Representation Transformer (OSRT), a highly efficient 3D-centric model in which individual object representations naturally emerge through novel view synthesis. |
Mehdi S. M. Sajjadi; Daniel Duckworth; Aravindh Mahendran; Sjoerd van Steenkiste; Filip Pavetić; Mario Lucic; Leonidas Guibas; Klaus Greff; Thomas Kipf; |
| 17 | Explicable Policy Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Prior work on explicable planning describes the ability of agents to respect their human teammate’s expectations by trading off task performance for more expected or "explicable" behaviors. In this paper, we introduce Explicable Policy Search (EPS) to significantly extend such an ability to a reinforcement learning (RL) setting and to handle stochastic domains with continuous state and action spaces. |
Ze Gong; Yu ("Tony") Zhang; |
| 18 | Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation By Anchored Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we further develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. |
Yongyi Su; Xun Xu; Kui Jia; |
| 19 | TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: TokenMixup is a general token-level augmentation method, which provides an efficient augmentation means for vision transformer models. |
Hyeong Kyu Choi; Joonmyung Choi; Hyunwoo Kim; |
| 20 | Optimistic Tree Searches for Combinatorial Black-Box Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel tree search algorithm for solving black-box combinatorial optimization problems |
Cedric Malherbe; Antoine Grosnit; Rasul Tutunov; Haitham Bou Ammar; Jun Wang; |
| 21 | Learning Robust Rule Representations for Abstract Reasoning Via Internal Inferences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel framework, ARII, that learns rule representations for Abstract Reasoning via Internal Inferences. |
Wenbo Zhang; likai tang; Site Mo; Xianggen Liu; Sen Song; |
| 22 | Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel adversarial style augmentation approach for domain generalization in semantic segmentation, which is easy to implement and can effectively improve the model performance on unseen real domains. |
Zhun Zhong; Yuyang Zhao; Gim Hee Lee; Nicu Sebe; |
| 23 | Amortized Projection Optimization for Sliced Wasserstein Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose to utilize amortized optimization to solve the computational issue of sliced Wasserstein in deep learning applications. |
Khai Nguyen; Nhat Ho; |
| 24 | OpenAUC: Towards AUC-Oriented Open-Set Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden’s index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. |
Zitai Wang; Qianqian Xu; Zhiyong Yang; Yuan He; Xiaochun Cao; Qingming Huang; |
| 25 | Don’t Pour Cereal Into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a differentiable linear temporal logic framework to provide explicit temporal constraints to action segmentation models, which results in improved performance. |
Ziwei Xu; Yogesh Rawat; Yongkang Wong; Mohan Kankanhalli; Mubarak Shah; |
| 26 | Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose convolution sliced Wasserstein between probability measures over images that are based on convolution operators. |
Khai Nguyen; Nhat Ho; |
| 27 | A Lower Bound of Hash Codes’ Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propsoe a lower bound of hash codes’ performance and a posterior estimation surrogate model over hash codes to improve hash learning. |
Xiaosu Zhu; Jingkuan Song; Yu Lei; Lianli Gao; Hengtao Shen; |
| 28 | I2Q: A Fully Decentralized Q-Learning Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To deal with non-stationarity, we first introduce stationary ideal transition probabilities, on which independent Q-learning could converge to the global optimum. Further, we propose a fully decentralized method, I2Q, which performs independent Q-learning on the modeled ideal transition function to reach the global optimum. |
Jiechuan Jiang; Zongqing Lu; |
| 29 | Unifying Voxel-based Representation with Transformer for 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present a unified framework for multi-modality 3D object detection, named UVTR. |
Yanwei Li; Yilun Chen; Xiaojuan Qi; Zeming Li; Jian Sun; Jiaya Jia; |
| 30 | Multiple-sample Neural Image Compression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to train NIC with multiple-sample importance weighted autoencoder (IWAE) target, which is tighter than ELBO and converges to log likelihood as sample size increases. |
Tongda Xu; Yan Wang; Dailan He; Chenjian Gao; Han Gao; Kunzan Liu; Hongwei Qin; |
| 31 | The Unreliability of Explanations in Few-Shot In-Context Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Explanations generated by LLMs can be unreliable, but they can still be useful as a way to verify GPT-3’s predictions post-hoc. |
Xi Ye; Greg Durrett; |
| 32 | Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our main contribution is to show that under proper choices of the regularization parameter, the gradient descent ascent algorithm converges to the Nash equilibrium of the original unregularized problem. |
Sihan Zeng; Thinh Doan; Justin Romberg; |
| 33 | The Price of Unfairness in Linear Bandits with Biased Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the problem of linear bandits with biased feedback and characterize it in terms of worst-case and gap-dependent regret. |
Solenne Gaucher; Alexandra Carpentier; Christophe Giraud; |
| 34 | Approximate Euclidean Lengths and Distances Beyond Johnson-Lindenstrauss Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We investigate techniques related to the Hutch++ algorithm to improve classical Johnson-Lindenstrauss approximations |
Aleksandros Sobczyk; Mathieu Luisier; |
| 35 | NS3: Neuro-symbolic Semantic Code Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, current language models are known to struggle with longer, compositional sentences, and multi-step reasoning. To overcome this limitation, we propose supplementing the query sentence with a layout of its semantic structure. |
Shushan Arakelyan; Anna Hakhverdyan; Miltiadis Allamanis; Christophe Hauser; Luis Garcia; Xiang Ren; |
| 36 | DeepInteraction: Exploring Multi-modal Interaction for 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce a novel 3D object detection architecture, dubbed as DeepInteraction, characterized by bilateral interaction and association throughout both representation encoding and decoding, in order to maximally exploit the inter-modal complementary property. |
Zeyu Yang; Jiaqi Chen; Zhenwei Miao; Wei Li; Xiatian Zhu; Li Zhang; |
| 37 | [Re] Exacerbating Algorithmic Bias Through Fairness Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The presented study evaluates ”Exacerbating Algorithmic Bias through Fairness Attacks” by Mehrabi et al. (2021) within the scope of the ML Reproducibility Challenge 2021. |
Angelos Nalmpantis; Apostolos Panagiotopoulos; John Gkountouras; Konstantinos Papakostas; |
| 38 | [Re] Differentiable Spatial Planning Using Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This report covers our reproduction effort of the paper ‘Differentiable Spatial Planning using Transformers’ by DOI Chaplot et al. [chaplot2021differentiable]. |
Rohit Ranjan; Himadri Bhakta; Animesh Jha; Parv Maheshwari; |
| 39 | FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a fast and memory-efficient exact attention algorithm by accounting for GPU memory reads/writes, yielding faster end-to-end training time and higher quality models with longer sequences. |
Tri Dao; Dan Fu; Stefano Ermon; Atri Rudra; Christopher Ré; |
| 40 | Distributed Learning of Finite Gaussian Mixtures Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this situation, the split-and-conquer strategy is among the most effective solutions to many statistical problems, including quantile processes, regression analysis, principal eigenspaces, and exponential families. This paper applies this strategy to develop a distributed learning procedure of finite Gaussian mixtures. |
Qiong Zhang; Jiahua Chen; |
| 41 | Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents Geo-SIC, the first deep learning model to learn deformable shapes in a deformation space for an improved performance of image classification. |
Jian Wang; Miaomiao Zhang; |
| 42 | Explainable Reinforcement Learning Via Model Transforms Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We use formal MDP abstractions and transforms, previously used for expediting planning, to automatically explain discrepancies between the behavior of a DRL agent and the behavior that is anticipated by an observer. |
Mira Finkelstein; Nitsan levy; Lucy Liu; Yoav Kolumbus; David Parkes; Jeffrey S Rosenschein; Sarah Keren; |
| 43 | Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We provide a uniyfing non-asymptotic analysis of recent variational inference methods based on Markovian gradients and propose an improved scheme. |
Kyurae Kim; Jisu Oh; Jacob Gardner; Adji Bousso Dieng; Hongseok Kim; |
| 44 | Local Latent Space Bayesian Optimization Over Structured Inputs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose LOL-BO, which adapts the notion of trust regions explored in recent work on high-dimensional Bayesian optimization to the structured setting. |
Natalie Maus; Haydn Jones; Juston Moore; Matt Kusner; John Bradshaw; Jacob Gardner; |
| 45 | A Hybrid Neural Autoencoder for Sensory Neuroprostheses and Its Applications in Bionic Vision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an encoder-decoder based stimulus encoding framework for sensory neuroprostheses and demonstrate its effectiveness for visual prostheses. |
Jacob Granley; Lucas Relic; Michael Beyeler; |
| 46 | Unsupervised Domain Adaptation for Semantic Segmentation Using Depth Distribution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides the existing methods that only use depth regression as an auxiliary task, we propose to use depth distribution density to support semantic segmentation. |
Quanliang Wu; Huajun Liu; |
| 47 | Visual Correspondence-based Explanations Improve AI Robustness and Human-AI Team Accuracy Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose and evaluate two novel, explainable image classifiers that explain before making decisions by computing explicit visual correspondence with examplars |
Mohammad Reza Taesiri; Giang Nguyen; Anh Nguyen; |
| 48 | Interaction Modeling with Multiplex Attention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we introduce a method for accurately modeling multi-agent systems. |
Fan-Yun Sun; Isaac Kauvar; Ruohan Zhang; Jiachen Li; Mykel J Kochenderfer; Jiajun Wu; Nick Haber; |
| 49 | FedSR: A Simple and Effective Domain Generalization Method for Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a domain generalization learning method suitable for federated learning by implicit representation alignment |
A. Tuan Nguyen; Ser Nam Lim; Philip Torr; |
| 50 | Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a latent adaptive structure-aware generative language model for universal information extraction. |
Hao Fei; Shengqiong Wu; Libo Qin; Jingye Li; Bobo Li; Fei Li; Meishan Zhang; Min Zhang; Tat-Seng Chua; |
| 51 | Physically-Based Face Rendering for NIR-VIS Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we reconstruct 3D face shape and reflectance from a large 2D facial dataset and introduce a novel method of transforming the VIS reflectance to NIR reflectance. |
Yunqi Miao; Alexandros Lattas; Jiankang Deng; Jungong Han; Stefanos Zafeiriou; |
| 52 | Unsupervised Learning From Incomplete Measurements for Inverse Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present necessary and sufficient conditions and an new unsupervised loss for learning from incomplete measurement data associated to multiple measurement operators. |
Julián Tachella; Dongdong Chen; Mike Davies; |
| 53 | Dynamic 3D from Monocular Video: Reality Check Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing works on dynamic view synthesis from monocular video actually evaluate on protocols that are essentially multi-view. We propose an actual monocular dataset and evaluation protocols that show there’s much room for improvement. |
Hang Gao; Ruilong Li; Shubham Tulsiani; Bryan Russell; Angjoo Kanazawa; |
| 54 | Peripheral Vision Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We explore blending human peripheral vision with machine vision for image recognition. |
Juhong Min; Yucheng Zhao; Chong Luo; Minsu Cho; |
| 55 | Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the problem of rich ads and give a simple truthful mechanism that achieves a constant of the optimal welfare. |
Gagan Aggarwal; Kshipra Bhawalkar; Aranyak Mehta; Divyarthi Mohan; Alexandros Psomas; |
| 56 | Are All Frames Equal? Active Sparse Labeling for Video Action Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose active sparse labeling (ASL), a novel active learning strategy for video action detection. |
Aayush Rana; Yogesh Rawat; |
| 57 | A Practical, Progressively-Expressive GNN Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Our work puts forth such a proposal: Namely, we first propose the (k, c)(=)-SETWL hierarchy with greatly reduced complexity from k-WL, achieved by moving from k-tuples of nodes to sets with =k nodes defined over =c connected components in the induced original graph. |
Lingxiao Zhao; Neil Shah; Leman Akoglu; |
| 58 | Constrained Predictive Coding As A Biologically Plausible Model of The Cortical Hierarchy Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: By employing a constraint on the latent variables, we derive an upper bound for the predictive-coding objective, which we use to obtain a biologically plausible neural network that shows excellent agreement with experimental observations. |
Siavash Golkar; Tiberiu Tesileanu; Yanis Bahroun; Anirvan Sengupta; Dmitri Chklovskii; |
| 59 | SPDNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the lack of a well-organized and annotated large-scale dataset hinders the training and verification of more effective and advancing deep-learning models for precipitation downscaling. To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named \emph{SPDNet}, which contains more than $62,400$ pairs of high-quality low/high-resolution precipitation maps for over $17$ years, ready to help the evolution of deep learning models in precipitation downscaling. |
Xuanhong Chen; Kairui Feng; Bingbing Ni; Naiyuan Liu; Yifan Lu; Ziang Liu; Zhengyan Tong; |
| 60 | Semi-supervised Vision Transformers at Scale Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our proposed method, dubbed Semi-ViT, achieves comparable or better performance than the CNN counterparts in the semi-supervised classification setting. |
Zhaowei Cai; Avinash Ravichandran; Paolo Favaro; Manchen Wang; Davide Modolo; Rahul Bhotika; Zhuowen Tu; Stefano Soatto; |
| 61 | Deep Fourier Up-Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This is the first attempt to propose a theoretically feasible Deep Fourier Up-sampling for multi-scale modeling. |
man zhou; Hu Yu; Jie Huang; Feng Zhao; Jinwei Gu; Chen Change Loy; Deyu Meng; Chongyi Li; |
| 62 | Free Probability As A Solution to The Problem of Tuning Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Stability (and hence performance) of NNs can be probed before training thanks to Free Probability Theory, which gives a computable metamodel in the infinite width regime. |
Reda CHHAIBI; Tariq Daouda; Ezechiel Kahn; |
| 63 | Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose Meta-Query-Net that adaptively finds the best balancing between purity and informativeness for open-set active learning. |
Dongmin Park; Yooju Shin; Jihwan Bang; Youngjun Lee; Hwanjun Song; Jae-Gil Lee; |
| 64 | Lazy and Fast Greedy MAP Inference for Determinantal Point Process Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We combine the lazy greedy algorithm and the Cholesky-factorization-based fast greedy algorithm for faster greedy DPP MAP inference. |
Shinichi Hemmi; Taihei Oki; Shinsaku Sakaue; Kaito Fujii; Satoru Iwata; |
| 65 | Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We show that label noise exists in adversarial training and can explain robust overfitting as well as its intriguing behaviors. |
Chengyu Dong; Liyuan Liu; Jingbo Shang; |
| 66 | Weakly Supervised Representation Learning with Sparse Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a natural estimation procedure based on this theory and illustrate it on low-dimensional synthetic and image-based experiments. |
Kartik Ahuja; Jason Hartford; Yoshua Bengio; |
| 67 | A Character-Level Length Control Algorithm for Non-Autoregressive Sentence Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a Non-Autoregressive summarization model with Character-level length Control (NACC) approach, which not only can control the number of characters in the model output explicitly but also is efficient in inference. |
Puyuan Liu; Xiang Zhang; Lili Mou; |
| 68 | Risk-Driven Design of Safety-Critical Perception Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Not all perception errors are equally unsafe. We combine closed-loop risk assessment with supervised learning to train safer perception systems. |
Anthony Corso; Sydney Katz; Craig Innes; Xin Du; Subramanian Ramamoorthy; Mykel J Kochenderfer; |
| 69 | Flatten The Curve: Efficiently Training Low-Curvature Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a practical method to train neural networks such that they have a low curvature, without losing predictive accuracy. |
Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret; |
| 70 | Self-explaining Deep Models with Logic Rule Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a framework for integrating self-explaining capabilities into a given deep model, so that it predicts accurately and explains with logic rules that are coherent with human decision logic. |
Seungeon Lee; Xiting Wang; Sungwon Han; Eunji Lee; Xiaoyuan Yi; Xing Xie; Meeyoung Cha; |
| 71 | Causal Disentanglement for Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper establishes the identifiability theories of unsupervised causal representation learning for sequential data and propose an implementation of the assumed causal model as a sequential deep generative model. |
Weiran Yao; Guangyi Chen; Kun Zhang; |
| 72 | FlowHMM: Flow-based Continuous Hidden Markov Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Continuous hidden Markov models (HMMs) assume that observations are generated from a mixture of Gaussian densities, limiting their ability to model more complex distributions. In this work, we address this shortcoming and propose novel continuous HMM models, dubbed FlowHMMs, that allow to learn general continuous observation densities without constraining them to follow a~Gaussian distribution or their mixtures. |
Pawel Lorek; Rafal Nowak; Tomasz Trzcinski; Maciej Zieba; |
| 73 | Markovian Interference in Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce an on-policy estimator: the Differences-In-Q’s (DQ) estimator. |
Vivek Farias; Andrew Li; Tianyi Peng; Andrew Zheng; |
| 74 | Lifting Weak Supervision To Structured Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We study weak supervision for structured prediction, obtaining favorable generalization guarantees despite using noisy pseudo-labels. |
Harit Vishwakarma; Frederic Sala; |
| 75 | Masked Autoencoders That Listen Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Audio-MAE learns SoTA embeddings from audio spectrograms. Without external pretraining, it achieves best performance with high masking ratio (80%) and decoders with local attention. Qualitative audible reconstructions demonstrate its effectiveness. |
Po-Yao Huang; Hu Xu; Juncheng Li; Alexei Baevski; Michael Auli; Wojciech Galuba; Florian Metze; Christoph Feichtenhofer; |
| 76 | Unsupervised Point Cloud Completion and Segmentation By Generative Adversarial Autoencoding Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a unsupervised method for point cloud completion and segmentation. |
Changfeng Ma; Yang Yang; Jie Guo; Fei Pan; Chongjun Wang; Yanwen Guo; |
| 77 | BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a simple LiDAR-camera fusion framework that overcomes the downside of previous fusion approaches. |
Tingting Liang; Hongwei Xie; Kaicheng Yu; Zhongyu Xia; Zhiwei Lin; Yongtao Wang; Tao Tang; Bing Wang; Zhi Tang; |
| 78 | Multi-agent Covering Option Discovery Based on Kronecker Product of Factor Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our key idea is to approximate the joint state space as the Kronecker product of individual agents’ state spaces, based on which we can directly estimate the Fiedler vector of the joint state space using the Laplacian spectrum of individual agents’ transition graphs. |
Jiayu Chen; Jingdi Chen; Tian Lan; Vaneet Aggarwal; |
| 79 | Neural Transmitted Radiance Fields Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim at addressing the problem of rendering novel transmitted views given a set of reflection-corrupted images. |
Chengxuan Zhu; Renjie Wan; Boxin Shi; |
| 80 | Neural Basis Models for Interpretability Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel subfamily of GAMs that utilizes basis decomposition of shape functions, called Neural Basis Models (NBMs). NBMs exploit the feature correlations and allow GAMs to scale by order of magnitude while preserving the interpretability. |
Filip Radenovic; Abhimanyu Dubey; Dhruv Mahajan; |
| 81 | On Divergence Measures for Bayesian Pseudocoresets Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We explored three divergence measures, reverse KLD, Wasserstein distance, and forward KLD to construct a Bayesian pseudocoreset. |
Balhae Kim; Jungwon Choi; Seanie Lee; Yoonho Lee; Jung-Woo Ha; Juho Lee; |
| 82 | Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose test-time prompt tuning (TPT) for CLIP to improve its zero-shot generalization. Our method works on a single test sample without the need for training data or annotations. |
Manli Shu; Chaowei Xiao; Weili Nie; De-An Huang; Zhiding Yu; Tom Goldstein; Anima Anandkumar; |
| 83 | Exact Solutions of A Deep Linear Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We find the analytical expression of the global minima of a deep feedforward linear network. |
Liu Ziyin; Botao Li; Xiangming Meng; |
| 84 | Maximum Likelihood Training of Implicit Nonlinear Diffusion Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a trainable implicit nonlinear diffusion process |
Dongjun Kim; Byeonghu Na; Se Jung Kwon; Dongsoo Lee; Wanmo Kang; Il-chul Moon; |
| 85 | Relation-Constrained Decoding for Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel algorithm RESEAL for relation-constrained decoding. |
Xiang Chen; Zhixian Yang; Xiaojun Wan; |
| 86 | Efficiency Ordering of Stochastic Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the notion of efficiency ordering as an alternative metric for comparing the performance of different stochastic input sequences for Stochastic Gradient Descent algorithm. |
Jie Hu; Vishwaraj Doshi; Do-Young Eun; |
| 87 | Mirror Descent with Relative Smoothness in Measure Spaces, with Application to Sinkhorn and EM Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We derive the convergence of mirror descent for relatively smooth and strongly convex pairs of functionals over measure spaces, applying it to Sinkhorn’s primal iterations and the EM algorithm through th KL. |
Pierre-Cyril Aubin-Frankowski; Anna Korba; Flavien Léger; |
| 88 | Interaction-Grounded Learning with Action-inclusive Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We proved that interaction-grounded learning is possible when the feedback has the full information of the action embedded in it. |
Tengyang Xie; Akanksha Saran; Dylan J Foster; Lekan Molu; Ida Momennejad; Nan Jiang; Paul Mineiro; John Langford; |
| 89 | Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a model-based reinforcement learning framework to derive untargeted poisoning attacks against federated learning (FL) systems. |
Henger Li; Xiaolin Sun; Zizhan Zheng; |
| 90 | On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the concept of preconditioning, we propose a novel method to significantly increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters. |
Markus Hiller; Mehrtash Harandi; Tom Drummond; |
| 91 | Cluster and Aggregate: Face Recognition with Large Probe Set Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a two-stage feature fusion paradigm, Cluster and Aggregate, that can both scale to large $N$ and maintain the ability to perform sequential inference with order invariance. |
Minchul Kim; Feng Liu; Anil K Jain; Xiaoming Liu; |
| 92 | Safety Guarantees for Neural Network Dynamic Systems Via Stochastic Barrier Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce a method of safety certification and control for neural network dynamic systems via stochastic barrier functions. |
Rayan Mazouz; Karan Muvvala; Akash Ratheesh Babu; Luca Laurenti; Morteza Lahijanian; |
| 93 | Online Frank-Wolfe with Arbitrary Delays Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A variant of online Frank-Wolfe for online learning with arbitrary delays is proposed, and it is robust to a relatively large amount of delay. |
Yuanyu Wan; Wei-Wei Tu; Lijun Zhang; |
| 94 | What Is A Good Metric to Study Generalization of Minimax Learners? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A fundamental question remains elusive: What is a good metric to study generalization of minimax learners? In this paper, we aim to answer this question by first showing that primal risk, a universal metric to study generalization in minimization problems, fails in simple examples of minimax problems. |
Asuman Ozdaglar; Sarath Pattathil; Jiawei Zhang; Kaiqing Zhang; |
| 95 | Globally Convergent Policy Search for Output Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop the first direct policy search algorithm which provably converges to the globally optimal dynamic filter for the classical problem of predicting the outputs of a linear dynamical system, given noisy, partial observations. |
Jack Umenberger; Max Simchowitz; Juan Perdomo; Kaiqing Zhang; Russ Tedrake; |
| 96 | Improving Neural Ordinary Differential Equations with Nesterov’s Accelerated Gradient Method Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the Nesterov neural ordinary differential equations (NesterovNODEs) whose layers solve the second-order ordinary differential equations limit of Nesterov’s accelerated gradient method for speeding up the training and inference of NODEs. |
Ho Huu Nghia Nguyen; Tan Nguyen; Huyen Vo; Stanley Osher; Thieu Vo; |
| 97 | Learning Structure from The Ground Up—Hierarchical Representation Learning By Chunking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the Gestalt principle of \textit{grouping by proximity} and theories of chunking in cognitive science, we propose a hierarchical chunking model (HCM). |
Shuchen Wu; Noemi Elteto; Ishita Dasgupta; Eric Schulz; |
| 98 | Are Defenses for Graph Neural Networks Robust? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness |
Felix Mujkanovic; Simon Geisler; Aleksandar Bojchevski; Stephan Günnemann; |
| 99 | Giving Feedback on Interactive Student Programs with Meta-Exploration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We build a system that interacts with a student program to find bugs and provides feedback with near human-level accuracy by showing that finding bugs is a meta-exploration problem |
Evan Liu; Moritz Stephan; Allen Nie; Chris Piech; Emma Brunskill; Chelsea Finn; |
| 100 | Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we extensively investigate the transfer performance of various types of self-supervised methods, e.g., MoCo and SimCLR, on three downstream tasks, including semantic segmentation, drivable area segmentation, and traffic object detection, on the large-scale driving dataset BDD100K. |
Xiwen Liang; Yangxin Wu; Jianhua Han; Hang Xu; Chunjing XU; Xiaodan Liang; |
| 101 | SPD Domain-specific Batch Normalization to Crack Interpretable Unsupervised Domain Adaptation in EEG Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose and evaluate (using EEG) an unsupervised domain adaptation framework around SPD domain-specific momentum batch normalization that enables end-to-end learning of tangent space mapping models. |
Reinmar Kobler; Jun-ichiro Hirayama; Qibin Zhao; Motoaki Kawanabe; |
| 102 | Gradient Estimation with Discrete Stein Operators Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To improve the quality of gradient estimation, we introduce a variance reduction technique based on Stein operators for discrete distributions. |
Jiaxin Shi; Yuhao Zhou; Jessica Hwang; Michalis Titsias; Lester Mackey; |
| 103 | Counterfactual Neural Temporal Point Process for Misinformation Impact Estimation on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a machine learning based counterfactual analysis framework to examine the misinformation’s causal influence on people. |
Yizhou Zhang; Defu Cao; Yan Liu; |
| 104 | Smoothed Embeddings for Certified Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we extend randomized smoothing to few-shot learning models that map inputs to normalized embeddings. |
Mikhail Pautov; Olesya Kuznetsova; Nurislam Tursynbek; Aleksandr Petiushko; Ivan Oseledets; |
| 105 | Training Subset Selection for Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show how to use pretrained representations to select high-quality subsets of weakly labeled training data. Training with these subsets improves the performance of weak supervision. |
Hunter Lang; Aravindan Vijayaraghavan; David Sontag; |
| 106 | Learning in Observable POMDPs, Without Computationally Intractable Oracles Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide a quasi-polynomial time algorithm for learning POMDPs. |
Noah Golowich; Ankur Moitra; Dhruv Rohatgi; |
| 107 | Robust Generalized Method of Moments: A Finite Sample Viewpoint Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a computationally efficient robustification of the generalized method of moments, which can tolerate a constant fraction of arbitrary outliers. |
Dhruv Rohatgi; Vasilis Syrgkanis; |
| 108 | Multi-Objective Deep Learning with Adaptive Reference Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Many deep learning models involve optimizing multiple objectives. Since objectives are often conflicting, we aim to get diverse and representative trade-off solutions among these objectives. |
Weiyu Chen; James Kwok; |
| 109 | Implicit Neural Representations with Levels-of-Experts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the limitation, we propose the Levels-of-Experts (LoE) framework, which is a novel coordinate-based representation consisting of an MLP with periodic, position-dependent weights arranged hierarchically. |
Zekun Hao; Arun Mallya; Serge Belongie; Ming-Yu Liu; |
| 110 | A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We investigate the stability properties of learned optimizers, and apply the insights gleaned to develop a learned optimization architecture that yields strong performance improvements over existing architectures. |
James Harrison; Luke Metz; Jascha Sohl-Dickstein; |
| 111 | A Solver-free Framework for Scalable Learning in Neural ILP Architectures Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: For learning constraints in a neural ILP architecture, we propose a scalable solver-free framework that doesn’t require calling the solver to compute gradients. |
Yatin Nandwani; Rishabh Ranjan; – Mausam; Parag Singla; |
| 112 | Sublinear Algorithms for Hierarchical Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The focus of this work is to study hierarchical clustering for massive graphs under three well-studied models of sublinear computation which focus on space, time, and communication, respectively, as the primary resources to optimize: (1) (dynamic) streaming model where edges are presented as a stream, (2) query model where the graph is queried using neighbor and degree queries, (3) massively parallel computation (MPC) model where the edges of the graph are partitioned over several machines connected via a communication channel. |
Arpit Agarwal; Sanjeev Khanna; Huan Li; Prathamesh Patil; |
| 113 | On Efficient Online Imitation Learning Via Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We give new positive and negative computational and statistical results on the fundamental feasibility of regret minimization in online imitation learning with discrete action spaces, in the general nonrealizable case. |
Yichen Li; Chicheng Zhang; |
| 114 | Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we conduct comprehensive analysis of existing domain bridging methods for domain adaptative semantic segmentation task and resort to two complementary data mixing techniques to propose a deliberated domain bridging strategy. |
Lin Chen; Zhixiang Wei; Xin Jin; Huaian Chen; Miao Zheng; Kai Chen; Yi Jin; |
| 115 | Generalization for Multiclass Classification with Overparameterized Linear Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Via an overparameterized linear model with Gaussian features, we provide conditions for good generalization for multiclass classification of minimum-norm interpolating solutions in an asymptotic setting where both the number of underlying features and the number of classes scale with the number of training points. |
Vignesh Subramanian; Rahul Arya; Anant Sahai; |
| 116 | Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we instead maintain a belief distribution over dynamics, and evaluate/optimize policy through biased sampling from the belief. |
Kaiyang Guo; Shao Yunfeng; Yanhui Geng; |
| 117 | Dance of SNN and ANN: Solving Binding Problem By Combining Spike Timing and Reconstructive Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a brain-inspired unsupervised hybrid neural network (HNN) that introduces temporal binding theory originated from neuroscience into ANNs by integrating spike timing dynamics (via spiking neural networks, SNNs) with reconstructive attention (by ANNs). |
Hao Zheng; Luping Shi; Rong Zhao; Hui Lin; |
| 118 | JAW: Guaranteed Predictive Inference Under Covariate Shift Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose JAWS, a series of wrapper methods for distribution-free uncertainty quantification under covariate shift, including: the jackknife+ with likelihood ratio weights; a computationally-efficient approximation; extensions to error assessment |
Drew Prinster; Anqi Liu; Suchi Saria; |
| 119 | Efficiently Factorizing Boolean Matrices Using Proximal Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel elastic-net based regularizer that permits efficient Boolean matrix factorization using proximal gradient descent. |
Sebastian Dalleiger; Jilles Vreeken; |
| 120 | Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a prompt certified machine unlearning algorithm, PCMU, which executes one-time operation of simultaneous training and unlearning in advance for a series of machine unlearning requests, without the knowledge of the removed/forgotten data. |
Zijie Zhang; Xin Zhao; Tianshi Che; Yang Zhou; Lingjuan Lyu; |
| 121 | Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and A Scalable Hyper-Ensemble Solution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We conduct a sensitivity analysis of unsupervised deep outlier detection methods to hyper-parameter (HP) settings, and design a scalable hyper-ensemble to circumvent the HP sensitivity issue in the literature. |
Xueying Ding; Lingxiao Zhao; Leman Akoglu; |
| 122 | Learning to Follow Instructions in Text-Based Games Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Such observations typically include instructions that, in a reinforcement learning (RL) setting, can directly or indirectly guide a player towards completing reward-worthy tasks. In this work, we study the ability of RL agents to follow such instructions. |
Mathieu Tuli; Andrew Li; Pashootan Vaezipoor; Toryn Klassen; Scott Sanner; Sheila McIlraith; |
| 123 | The Importance of Baselines in Policy Gradients Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our first contribution is to show that the \emph{state value} baseline allows on-policy stochastic \emph{natural} policy gradient (NPG) to converge to an optimal policy at an $O(1/t)$ rate, which was not previously known. |
Jincheng Mei; Wesley Chung; Valentin Thomas; Bo Dai; Csaba Szepesvari; Dale Schuurmans; |
| 124 | Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. |
Daniel Lee; Georgy Noarov; Mallesh Pai; Aaron Roth; |
| 125 | Learning from A Sample in Online Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: But can we go beyond the worst-case? In this work we give algorithms that perform substantially better when a $p$-fraction of the input is given as a sample: the algorithm use this sample to \emph{learn} a good strategy to use for the rest of the input. |
C.J. Argue; Anupam Gupta; Alan Frieze; Christopher Seiler; |
| 126 | Robustness Disparities in Face Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Additionally, no prior work has focused on the robustness of these systems under various perturbations and corruptions, which leaves open the question of how various people are impacted by these phenomena. We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. |
Samuel Dooley; George Z Wei; Tom Goldstein; John Dickerson; |
| 127 | SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To bridge this gap, we develop an easy-to-use library that includes five high-fidelity simulation environments: BeerFMTEnv, ReactorEnv, AtropineEnv, PenSimEnv and mAbEnv, which cover a wide range of manufacturing processes. |
Mohan Zhang; Xiaozhou Wang; Benjamin Decardi-Nelson; Bo Song; An Zhang; Jinfeng Liu; Sile Tao; Jiayi Cheng; Xiaohong Liu; Dengdeng Yu; Matthew Poon; Animesh Garg; |
| 128 | GriddlyJS: A Web IDE for Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, existing environments often require complex build processes, making reproducing results difficult. To address these issues, we introduce GriddlyJS, a web-based Integrated Development Environment (IDE) based on the Griddly engine. |
Christopher Bamford; Minqi Jiang; Mikayel Samvelyan; Tim Rocktäschel; |
| 129 | Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Avalon is a benchmark for generalization in RL where all individual tasks are constructed via finely controlled procedural generation of environments. |
Joshua Albrecht; Abraham Fetterman; Bryden Fogelman; Ellie Kitanidis; Bartosz Wróblewski; Nicole Seo; Michael Rosenthal; Maksis Knutins; Zack Polizzi; James Simon; Kanjun Qiu; |
| 130 | CLEVRER-Humans: Describing Physical and Causal Events The Human Way Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The CLEVRER-Humans benchmark is a video reasoning dataset for causal judgment of physical events with human labels. |
Jiayuan Mao; Xuelin Yang; Xikun Zhang; Noah Goodman; Jiajun Wu; |
| 131 | Ambiguous Images With Human Judgments for Robust Visual Event Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a procedure for creating datasets of ambiguous images and use it to produce DAI (Dataset of Ambiguous Images), a collection of noisy images extracted from videos and corresponding human uncertainty judgments. |
Kate Sanders; Reno Kriz; Anqi Liu; Benjamin Van Durme; |
| 132 | Finding Naturally Occurring Physical Backdoors in Image Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We discover and validate the existence of natural backdoors in existing image datasets. |
Emily Wenger; Roma Bhattacharjee; Arjun Nitin Bhagoji; Josephine Passananti; Emilio Andere; Heather Zheng; Ben Zhao; |
| 133 | A Large Scale Search Dataset for Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: we introduce a new large-scale unbiased learning to rank dataset with rich real-world user feedback and sufficient display information. |
Lixin Zou; Haitao Mao; Xiaokai Chu; Jiliang Tang; Wenwen Ye; Shuaiqiang Wang; Dawei Yin; |
| 134 | SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We are releasing SoundSpaces 2.0: a fast, continuous, configurable and generalizable audio-visual simulation platform for visual acoustic machine learning research, e.g., audio-visual navigation, far-field speech recognition, and acoustic matching. |
Changan Chen; Carl Schissler; Sanchit Garg; Philip Kobernik; Alexander Clegg; Paul Calamia; Dhruv Batra; Philip Robinson; Kristen Grauman; |
| 135 | ActionNet: A Multimodal Dataset for Human Activities Using Wearable Sensors in A Kitchen Environment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces ActionNet, a multimodal dataset and recording framework with an emphasis on wearable sensing in a kitchen environment. |
Joseph DelPreto; Chao Liu; Yiyue Luo; Michael Foshey; Yunzhu Li; Antonio Torralba; Wojciech Matusik; Daniela Rus; |
| 136 | SkinCon: A Skin Disease Dataset Densely Annotated By Domain Experts for Fine-grained Debugging and Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: SkinCon is a skin disease dataset densely annotated by domain experts for developing interpretability/explainability methods and fine-grained error analysis. |
Roxana Daneshjou; Mert Yuksekgonul; Zhuo Ran Cai; Roberto Novoa; James Zou; |
| 137 | CEDe: A Collection of Expert-curated Datasets with Atom-level Entity Annotations for Optical Chemical Structure Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A collection of datasets containing more than 700,000 atom-level entity annotations and their corresponding bounding boxes. This labels constitute all the necessary information for complete chemical graph reconstruction. |
Rodrigo Hormazabal; Changyoung Park; Soonyoung Lee; Sehui Han; Yeonsik Jo; Jaewan Lee; Ahra Jo; Seung Hwan Kim; Jaegul Choo; Moontae Lee; Honglak Lee; |
| 138 | MVP-N: A Dataset and Benchmark for Real-World Multi-View Object Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a dataset and benchmark for multi-view object classification. |
REN WANG; Jiayue Wang; Tae Sung Kim; JINSUNG KIM; Hyuk-Jae Lee; |
| 139 | A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Here we present the first large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task. |
Wonseok Hwang; Dongjun Lee; Kyoungyeon Cho; Hanuhl Lee; Minjoon Seo; |
| 140 | Kantorovich Strikes Back! Wasserstein GANs Are Not Optimal Transport? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we address these questions. We construct 1-Lipschitz functions and use them to build ray monotone transport plans. |
Alexander Korotin; Alexander Kolesov; Evgeny Burnaev; |
| 141 | AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. |
Yuanfeng Ji; Haotian Bai; Chongjian GE; Jie Yang; Ye Zhu; Ruimao Zhang; Zhen Li; Lingyan Zhanng; Wanling Ma; Xiang Wan; Ping Luo; |
| 142 | TGEA 2.0: A Large-Scale Diagnostically Annotated Dataset with Benchmark Tasks for Text Generation of Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: With the diagnostically annotated dataset, we propose 5 diagnosis benchmark tasks (i.e., erroneous text detection, MiSEW extraction, erroneous span location and correction together with error type classification) and 2 pathology mitigation benchmark tasks (pairwise comparison and word prediction). |
Huibin Ge; Xiaohu Zhao; Chuang Liu; Yulong Zeng; Qun Liu; Deyi Xiong; |
| 143 | The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. |
Hugo Laurençon; Lucile Saulnier; Thomas Wang; Christopher Akiki; Albert Villanova del Moral; Teven Le Scao; Leandro Von Werra; Chenghao Mou; Eduardo González Ponferrada; Huu Nguyen; Jörg Frohberg; Mario Šaško; Quentin Lhoest; Angelina McMillan-Major; Gerard Dupont; Stella Biderman; Anna Rogers; Loubna Ben allal; Francesco De Toni; Giada Pistilli; Olivier Nguyen; Somaieh Nikpoor; Maraim Masoud; Pierre Colombo; Javier de la Rosa; Paulo Villegas; Tristan Thrush; Shayne Longpre; Sebastian Nagel; Leon Weber; Manuel Muñoz; Jian Zhu; Daniel Van Strien; Zaid Alyafeai; Khalid Almubarak; Minh Chien Vu; Itziar Gonzalez-Dios; Aitor Soroa; Kyle Lo; Manan Dey; Pedro Ortiz Suarez; Aaron Gokaslan; Shamik Bose; David Adelani; Long Phan; Hieu Tran; Ian Yu; Suhas Pai; Jenny Chim; Violette Lepercq; Suzana Ilic; Margaret Mitchell; Alexandra V Luccioni; Yacine Jernite; |
| 144 | NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we construct a unified, expressive yet compact search space, covering 26,206 unique graph neural network (GNN) architectures and propose a principled evaluation protocol. |
Yijian Qin; Ziwei Zhang; Xin Wang; Zeyang Zhang; Wenwu Zhu; |
| 145 | Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This could lead to less optimal baselines, hindering the fair and faithful evaluations of newly designed methodologies. To address this problem, this work presents the \textit{first} comprehensive benchmarking study from three under-explored perspectives beyond algorithms. |
Hui En Pang; Zhongang Cai; Lei Yang; Tianwei Zhang; Ziwei Liu; |
| 146 | MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent environment simulates the target coverage control problems in the real world. |
Xuehai Pan; Mickel Liu; Fangwei Zhong; Yaodong Yang; Song-Chun Zhu; Yizhou Wang; |
| 147 | How Transferable Are Video Representations Based on Synthetic Data? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose SynAPT, a novel benchmark for action recognition based on a combination of existing synthetic datasets, in which a model is pre-trained on synthetic videos rendered by various graphics simulators, and then transferred to a set of downstream action recognition datasets, containing different categories than the synthetic data. |
Yo-whan Kim; Samarth Mishra; SouYoung Jin; Rameswar Panda; Hilde Kuehne; Leonid Karlinsky; Venkatesh Saligrama; Kate Saenko; Aude Oliva; Rogerio Feris; |
| 148 | NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We create a benchmark suite for zero-cost proxies, and we use it to show how to effectively combine them to improve performance. |
Arjun Krishnakumar; Colin White; Arber Zela; Renbo Tu; Mahmoud Safari; Frank Hutter; |
| 149 | Pile of Law: Learning Responsible Data Filtering from The Law and A 256GB Open-Source Legal Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work we have examine how the law and legal data can inform data filtering practices and provide an extensive 256GB legal dataset (the Pile of Law) that can be used to learn these norms, and for pretraining. |
Peter Henderson; Mark Krass; Lucia Zheng; Neel Guha; Christopher D Manning; Dan Jurafsky; Daniel Ho; |
| 150 | Breaking Bad: A Dataset for Geometric Fracture and Reassembly Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce Breaking Bad, a large-scale dataset of fractured objects. |
Silvia Sellán; Yun-Chun Chen; Ziyi Wu; Animesh Garg; Alec Jacobson; |
| 151 | Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and photo critiques. |
Daniel Vera Nieto; Luigi Celona; Clara Fernandez Labrador; |
| 152 | EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. |
Ahmad Darkhalil; Dandan Shan; Bin Zhu; Jian Ma; Amlan Kar; Richard Higgins; Sanja Fidler; David Fouhey; Dima Damen; |
| 153 | PFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose the first comprehensive benchmark for personalized Federated Learning, containing more than 10 datasets, 20 pFL methods, and systematic evaluation with highlighted benefits and potential of pFL. |
Daoyuan Chen; Dawei Gao; Weirui Kuang; Yaliang Li; Bolin Ding; |
| 154 | Touch and Go: Learning from Human-Collected Vision and Touch Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce “Touch and Go”, a human-collected dataset containing paired visual and tactile data from real-world scenes. |
Fengyu Yang; Chenyang Ma; Jiacheng Zhang; Jing Zhu; Wenzhen Yuan; Andrew Owens; |
| 155 | DDXPlus: A New Dataset For Automatic Medical Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present a large-scale synthetic dataset of roughly 1.3 million patients that includes a differential diagnosis, along with the ground truth pathology, symptoms and antecedents for each patient. |
Arsene Fansi Tchango; Rishab Goel; Zhi Wen; Julien Martel; Joumana Ghosn; |
| 156 | AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce AutoWS-Bench-101: a benchmarking framework for automated weak supervision techniques on diverse tasks. |
Nicholas Roberts; Xintong Li; Tzu-Heng Huang; Dyah Adila; Spencer Schoenberg; Cheng-Yu Liu; Lauren Pick; Haotian Ma; Aws Albarghouthi; Frederic Sala; |
| 157 | This Is The Way: Designing and Compiling LEPISZCZE, A Comprehensive NLP Benchmark for Polish Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we introduce LEPISZCZE (lepiszczeis the Polish word for glew, the Middle English predecessor of glue) a new, comprehensive benchmark for Polish NLP with a large variety of tasks and high-quality operationalization of the benchmark. |
Łukasz Augustyniak; Kamil Tagowski; Albert Sawczyn; Denis Janiak; Roman Bartusiak; Adrian Szymczak; Arkadiusz Janz; Piotr Szymański; Marcin Wątroba; Mikołaj Morzy; Tomasz Kajdanowicz; Maciej Piasecki; |
| 158 | Evaluating Out-of-Distribution Performance on Document Image Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Our paper introduces new out-of-distribution data for evaluating document classifiers, and finds that models trained on RVL-CDIP but tested on our new out-of-distribution data tend to underperform. |
Stefan Larson; Yi Yang Gordon Lim; Yutong Ai; David Kuang; Kevin Leach; |
| 159 | FETA: Towards Specializing Foundational Models for Expert Task Applications Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a first of its kind FETA benchmark built around the task of teaching FMs to understand technical documentation, via learning to match their graphical illustrations to corresponding language descriptions. |
Amit Alfassy; Assaf Arbelle; Oshri Halimi; Sivan Harary; Roei Herzig; Eli Schwartz; Rameswar Panda; Michele Dolfi; Christoph Auer; Peter Staar; Kate Saenko; Rogerio Feris; Leonid Karlinsky; |
| 160 | Why Do Tree-based Models Still Outperform Deep Learning on Typical Tabular Data? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Results show that tree-based models remain state-of-the-art on medium-sized data (10K samples) even without accounting for their superior speed. To understand this gap, we conduct an empirical investigation into the differing inductive biases of tree-based models and neural networks. |
Leo Grinsztajn; Edouard Oyallon; Gael Varoquaux; |
| 161 | Myriad: A Real-world Testbed to Bridge Trajectory Optimization and Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a testbed to benchmark imitation learning and reinforcement learning algorithms against trajectory optimization-based methods in real-world environments. |
Nikolaus Howe; Simon Dufort-Labbé; Nitarshan Rajkumar; Pierre-Luc Bacon; |
| 162 | METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we release METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19 related tweets. |
Peilin Zhou; Zeqiang Wang; Dading Chong; Zhijiang Guo; Yining Hua; Zichang Su; Zhiyang Teng; Jiageng Wu; Jie Yang; |
| 163 | A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a comprehensive and fair benchmark study on large-scale graph training and further propose a new layer-wise training manner the achieves new SOTA performance on large-scale graph datasets. |
Keyu Duan; Zirui Liu; Peihao Wang; Wenqing Zheng; Kaixiong Zhou; Tianlong Chen; Xia Hu; Zhangyang Wang; |
| 164 | CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to Multiple Real-World Domains Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. |
Bonifaz Stuhr; Johann Haselberger; Julian Gebele; |
| 165 | Hard ImageNet: Segmentations for Objects with Strong Spurious Cues Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The severity of this problem varies significantly by class. We identify $15$ classes in ImageNet with very strong spurious cues, and collect segmentation masks for these challenging objects to form \emph{Hard ImageNet}. |
Mazda Moayeri; Sahil Singla; Soheil Feizi; |
| 166 | MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Although online handwriting verification has made great progress recently, the verification performances are still far behind the real usage owing to the small scale of the datasets as well as the limited biometric mediums. Therefore, this paper proposes a new handwriting verification benchmark dataset named Multimodal Signature and Digit String (MSDS), which consists of two subsets: MSDS-ChS (Chinese Signatures) and MSDS-TDS (Token Digit Strings), contributed by 402 users, with 20 genuine samples and 20 skilled forgeries per user per subset. |
Peirong Zhang; Jiajia Jiang; Yuliang Liu; Lianwen Jin; |
| 167 | SurDis: A Surface Discontinuity Dataset for Wearable Technology to Assist Blind Navigation in Urban Environments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce SurDis, a novel dataset of depth maps and stereo images that exemplifies the issue of surface discontinuity in the urban areas of Klang Valley, Malaysia. |
Kuan Yew Leong; Siew Mooi Lim; |
| 168 | ADBench: Anomaly Detection Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. |
Songqiao Han; Xiyang Hu; Hailiang Huang; Minqi Jiang; Yue Zhao; |
| 169 | AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a high fidelity aerodynamic dataset of Reynolds-Averaged Navier–Stokes (RANS) simulations over airfoils |
Florent Bonnet; Jocelyn Mazari; Paola Cinnella; Patrick Gallinari; |
| 170 | A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address these issues, we categorize existing works into three practical scenarios in which attackers release datasets, pre-trained models, and fine-tuned models respectively, then discuss their unique evaluation methodologies. |
Ganqu Cui; Lifan Yuan; Bingxiang He; Yangyi Chen; Zhiyuan Liu; Maosong Sun; |
| 171 | MBW: Multi-view Bootstrapping in The Wild Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The approach, however, is based on calibrated cameras and rigid geometry, making it expensive, difficult to manage, and impractical in real-world scenarios. In this paper, we address these bottlenecks by combining a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark estimates from videos with only two or three uncalibrated, handheld cameras. |
Mosam Dabhi; Chaoyang Wang; Tim Clifford; László Jeni; Ian Fasel; Simon Lucey; |
| 172 | Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We created the first blockchain ML-Ready dataset platform |
Kiarash Shamsi; Friedhelm Victor; Murat Kantarcioglu; Yulia Gel; Cuneyt G Akcora; |
| 173 | Learning Long-Term Crop Management Strategies with CyclesGym Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce CYCLESGYM, an RL environment based on the multi-year, multi-crop CGM Cycles. |
Matteo Turchetta; Luca Corinzia; Scott Sussex; Amanda Burton; Juan Herrera; Ioannis Athanasiadis; Joachim M Buhmann; Andreas Krause; |
| 174 | LIPS – Learning Industrial Physical Simulation Benchmark Suite Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a new benchmark suite "Learning Industrial Physical Simulations" (LIPS), whose purpose is to assess the quality of surrogate models for emulation of a physical system following various evaluation criteria categories |
Milad LEYLI ABADI; Antoine Marot; Jérôme Picault; David Danan; Mouadh Yagoubi; Benjamin Donnot; Seif Attoui; Pavel Dimitrov; Asma Farjallah; Clement Etienam; |
| 175 | FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.FLamby encompasses 7 healthcare datasets with natural splits, covering multiple tasks, modalities, and data volumes, each accompanied with baseline training code. |
Jean Ogier du Terrail; Samy-Safwan Ayed; Edwige Cyffers; Felix Grimberg; Chaoyang He; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; Santiago Silva; Maria Teleńczuk; Shadi Albarqouni; Salman Avestimehr; Aurélien Bellet; Aymeric Dieuleveut; Martin Jaggi; Sai Praneeth Karimireddy; Marco Lorenzi; Giovanni Neglia; Marc Tommasi; Mathieu Andreux; |
| 176 | PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This work proposes a comprehensive and multi-task benchmark for protein sequence understanding, which studies both single-task and multi-task learning. |
Minghao Xu; Zuobai Zhang; Jiarui Lu; Zhaocheng Zhu; Yangtian Zhang; Ma Chang; Runcheng Liu; Jian Tang; |
| 177 | MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new dataset and framework for evaluating video-language models on activity recognition at multiple levels of granularity |
Zelun Luo; Zane Durante; Linden Li; Wanze Xie; Ruochen Liu; Emily Jin; Zhuoyi Huang; Lun Yu Li; Jiajun Wu; Juan Carlos Niebles; Ehsan Adeli; Fei-Fei Li; |
| 178 | Towards Open Set 3D Learning: Benchmarking and Understanding Semantic Novelty Detection on Pointclouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a novel testbed for semantic novelty detection that considers several settings with increasing difficulties in terms of category semantic shift, and covers both in-domain (synthetic-to-synthetic, real-to-real) and cross-domain (synthetic- to-real) scenarios. |
Antonio Alliegro; Francesco Cappio Borlino; Tatiana Tommasi; |
| 179 | Unravelling The Performance of Physics-informed Graph Neural Networks for Dynamical Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Our study demonstrates that GNNs with additional inductive biases, such as explicit constraints and decoupling of kinetic and potential energies, exhibit significantly enhanced performance. |
Abishek Thangamuthu; Gunjan Kumar; Suresh Bishnoi; Ravinder Bhattoo; N M Anoop Krishnan; Sayan Ranu; |
| 180 | A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we introduce a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. |
Konstantina Dritsa; Aikaterini Thoma; Ioannis Pavlopoulos; Panos Louridas; |
| 181 | A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To raise awareness of publicly available criminal justice datasets and encourage their responsible use, we conduct a survey, consider contexts, highlight potential uses, and identify gaps and limitations. |
Miri Zilka; Bradley Butcher; Adrian Weller; |
| 182 | A Benchmark for Compositional Visual Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. |
Aimen Zerroug; Mohit Vaishnav; Julien Colin; Sebastian Musslick; Thomas Serre; |
| 183 | FACT: Learning Governing Abstractions Behind Integer Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Integer sequences are of central importance to the modeling of concepts admitting complete finitary descriptions. We introduce a novel view on the learning of such concepts and lay down a set of benchmarking tasks aimed at conceptual understanding by machine learning models. |
Peter Belcak; Ard Kastrati; Flavio Schenker; Roger Wattenhofer; |
| 184 | XView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. |
Fernando Paolo; Tsu-ting Tim Lin; Ritwik Gupta; Bryce Goodman; Nirav Patel; Daniel Kuster; David Kroodsma; Jared Dunnmon; |
| 185 | BLOX: Macro Neural Architecture Search Benchmark and Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To provide a systematic study of the performance of NAS algorithms on a macro search space, we release Blox – a benchmark that consists of 91k unique models trained on the CIFAR-100 dataset. |
Thomas Chau; Łukasz Dudziak; Hongkai Wen; Nicholas Lane; Mohamed Abdelfattah; |
| 186 | ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we design a suite of benchmarks that can be used to pre-train and evaluate echocardiographic representations with respect to various clinically-relevant tasks using publicly accessible data sets. |
Ahmed M. Alaa; Anthony Philippakis; David Sontag; |
| 187 | BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present BOND, a comprehensive benchmark for unsupervised node outlier detection on attributed static graphs. |
Kay Liu; Yingtong Dou; Yue Zhao; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng; Kai Shu; Lichao Sun; Jundong Li; George H Chen; Zhihao Jia; Philip S Yu; |
| 188 | Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a bimanual dexterous manipulation benchmark according to literature from cognitive science for comprehensive reinforcement learning research. |
Yuanpei Chen; Tianhao Wu; Shengjie Wang; Xidong Feng; Jiechuan Jiang; Zongqing Lu; Stephen McAleer; Hao Dong; Song-Chun Zhu; Yaodong Yang; |
| 189 | APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The lack of APT datasets hinders the development and evaluation of video-based animal pose estimation and tracking methods, limiting the applications in real world, e.g., understanding animal behavior in wildlife conservation. To fill this gap, we make the first step and propose APT-36K, i.e., the first large-scale benchmark for animal pose estimation and tracking. |
Yuxiang Yang; Junjie Yang; Yufei Xu; Jing Zhang; Long Lan; Dacheng Tao; |
| 190 | JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present JAHS-Bench-201, the first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search, built to also facilitate research on multi-objective, cost-aware and (multi) multi-fidelity optimization algorithms. |
Archit Bansal; Danny Stoll; Maciej Janowski; Arber Zela; Frank Hutter; |
| 191 | Forecasting Future World Events With Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a dataset for forecasting diverse future world events. |
Andy Zou; Tristan Xiao; Ryan Jia; Joe Kwon; Mantas Mazeika; Richard Li; Dawn Song; Jacob Steinhardt; Owain Evans; Dan Hendrycks; |
| 192 | CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As models can use complementary information from multimodal cues to recognize referring expressions, generating multimodal data from multiple views can help to develop robust models. To address these critical issues, in this paper, we present a novel embodied simulator, CAESAR, to generate multimodal referring expressions containing both verbal utterances and nonverbal cues captured from multiple views. |
Md Mofijul Islam; Reza Mirzaiee; Alexi Gladstone; Haley Green; Tariq Iqbal; |
| 193 | How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce two large-scale video datasets for predicting how videos would the emotional state and wellbeing of viewers. |
Mantas Mazeika; Eric Tang; Andy Zou; Steven Basart; Jun Shern Chan; Dawn Song; David Forsyth; Jacob Steinhardt; Dan Hendrycks; |
| 194 | MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: MineDojo is a new framework built on the Minecraft game for developing open-ended, generally capable embodied agents. |
Linxi Fan; Guanzhi Wang; Yunfan Jiang; Ajay Mandlekar; Yuncong Yang; Haoyi Zhu; Andrew Tang; De-An Huang; Yuke Zhu; Anima Anandkumar; |
| 195 | StrokeRehab: A Benchmark Dataset for Sub-second Action Identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a new benchmark dataset for the identification of subtle and short-duration actions. We also propose a novel seq2seq approach, which outperforms the existing methods on the new as well as standard benchmark datasets. |
Aakash Kaku; Kangning Liu; Avinash Parnandi; Haresh Rengaraj Rajamohan; Kannan Venkataramanan; Anita Venkatesan; Audre Wirtanen; Natasha Pandit; Heidi Schambra; Carlos Fernandez-Granda; |
| 196 | TwiBot-22: Towards Graph-Based Twitter Bot Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We make the case for graph-based Twitter bot detection and propose a graph-based benchmark TwiBot-22, which addresses the issues of limited dataset scale, incomplete graph structure, and low annotation quality in previous datasets. |
Shangbin Feng; Zhaoxuan Tan; Herun Wan; Ningnan Wang; Zilong Chen; Binchi Zhang; Qinghua Zheng; Wenqian Zhang; Zhenyu Lei; Shujie Yang; Xinshun Feng; Qingyue Zhang; Hongrui Wang; Yuhan Liu; Yuyang Bai; Heng Wang; Zijian Cai; Yanbo Wang; Lijing Zheng; Zihan Ma; Jundong Li; Minnan Luo; |
| 197 | TAP-Vid: A Benchmark for Tracking Any Point in A Video Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we first formalize the problem, naming it tracking any point (TAP). We introduce a companion benchmark,TAP-Vid, which is composed of both real-world videos with accurate human annotations of point tracks, and synthetic videos with perfect ground-truth point tracks. |
Carl Doersch; Ankush Gupta; Larisa Markeeva; Adria Recasens; Lucas Smaira; Yusuf Aytar; Joao Carreira; Andrew Zisserman; Yi Yang; |
| 198 | Is One Annotation Enough? – A Data-centric Image Classification Benchmark for Noisy and Ambiguous Label Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The aggregation of such annotations to determine the label of an image leads to a lower data quality. We propose a data-centric image classification benchmark with nine real-world datasets and multiple annotations per image to allow researchers to investigate and quantify the impact of such data quality issues. |
Lars Schmarje; Vasco Grossmann; Claudius Zelenka; Sabine Dippel; Rainer Kiko; Mariusz Oszust; Matti Pastell; Jenny Stracke; Anna Valros; Nina Volkmann; Reinhard Koch; |
| 199 | Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: A large-scale Chinese cross-modal dataset, called Wukong, containing 100 million image-text pairs is released. Models with either global similarity or token-wise similarity are pre-trained and benchmarked on extensive downstream tasks. |
Jiaxi Gu; Xiaojun Meng; Guansong Lu; Lu Hou; Niu Minzhe; Xiaodan Liang; Lewei Yao; Runhui Huang; Wei Zhang; Xin Jiang; Chunjing XU; Hang Xu; |
| 200 | BackdoorBench: A Comprehensive Benchmark of Backdoor Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We also provide comprehensive evaluations of every pair of 8 attacks against 9 defenses, with 5 poisoning ratios, based on 5 models and 4 datasets, thus 8,000 pairs of evaluations in total. We present abundant analysis from different perspectives about these 8,000 evaluations, studying the effects of different factors in backdoor learning. |
Baoyuan Wu; Hongrui Chen; Mingda Zhang; Zihao Zhu; Shaokui Wei; Danni Yuan; Chao Shen; |
| 201 | M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The lack of publicly available high-quality and accurately labeled datasets has long been a major bottleneck for singing voice synthesis (SVS). To tackle this problem, we present M4Singer, a free-to-use Multi-style, Multi-singer Mandarin singing collection with elaborately annotated Musical scores as well as its benchmarks. |
Lichao Zhang; Ruiqi Li; Shoutong Wang; Liqun Deng; Jinglin Liu; Yi Ren; Jinzheng He; Rongjie Huang; Jieming Zhu; Xiao Chen; Zhou Zhao; |
| 202 | IKEA-Manual: Seeing Shape Assembly Step By Step Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We identify that this is due to 1) a lack of realistic 3D assembly objects that have paired manuals and 2) the difficulty of extracting structured information from purely image-based manuals. Motivated by this observation, we present IKEA-Manual, a dataset consisting of 102 IKEA objects paired with assembly manuals. |
Ruocheng Wang; Yunzhi Zhang; Jiayuan Mao; Ran Zhang; Chin-Yi Cheng; Jiajun Wu; |
| 203 | HandMeThat: Human-Robot Communication in Physical and Social Environments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: HandMeThat is a benchmark for evaluating instruction understanding and following in physical and social environments. |
Yanming Wan; Jiayuan Mao; Josh Tenenbaum; |
| 204 | How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we establish benchmarks for both real-time and life-long continual visual learning. |
Chengxu Zhuang; Ziyu Xiang; Yoon Bai; Xiaoxuan Jia; Nicholas Turk-Browne; Kenneth Norman; James J DiCarlo; Dan Yamins; |
| 205 | CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents CLiMB, a benchmark to study the challenge of learning vision-language tasks in a continual learning setting, and to systematically evaluate how upstream continual learning can rapidly transfer to new multi- and unimodal tasks. |
Tejas Srinivasan; Ting-Yun Chang; Leticia Pinto Alva; Georgios Chochlakis; Mohammad Rostami; Jesse Thomason; |
| 206 | Long Range Graph Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present the Long Range Graph Benchmark (LRGB) with 5 datasets that can be used for the development of models enabling long range dependencies in graphs, like Graph Transformers. |
Vijay Prakash Dwivedi; Ladislav Rampášek; Mikhail Galkin; Ali Parviz; Guy Wolf; Anh Tuan Luu; Dominique Beaini; |
| 207 | Wild-Time: A Benchmark of In-the-Wild Distribution Shift Over Time Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address this gap, we curate Wild-Time, a benchmark of 7 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including drug discovery, patient prognosis, and news classification. On these datasets, we systematically benchmark 13 approaches with various inductive biases. |
Huaxiu Yao; Caroline Choi; Bochuan Cao; Yoonho Lee; Pang Wei Koh; Chelsea Finn; |
| 208 | ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in The Wild Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose ConfLab (Conference Living Lab) as a new concept for in-the-wild recording of real-life social human behavior, and provide a dataset from the first edition of ConfLab at a major international conference. |
Chirag Raman; Jose Vargas Quiros; Stephanie Tan; Ashraful Islam; Ekin Gedik; Hayley Hung; |
| 209 | Communicating Natural Programs to Humans and Machines Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We collect a dataset called LARC, consisting of natural language instructions, used by end-users to instruct each-other how to solve the ARC (a notoriously difficult dataset for AI and program synthesis) tasks |
Sam Acquaviva; Yewen Pu; Marta Kryven; Theodoros Sechopoulos; Catherine Wong; Gabrielle Ecanow; Maxwell Nye; Michael Tessler; Josh Tenenbaum; |
| 210 | USB: A Unified Semi-supervised Learning Benchmark for Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) for classification by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate the dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation of these SSL methods. |
Yidong Wang; Hao Chen; Yue Fan; Wang SUN; Ran Tao; Wenxin Hou; Renjie Wang; Linyi Yang; Zhi Zhou; Lan-Zhe Guo; Heli Qi; Zhen Wu; Yu-Feng Li; Satoshi Nakamura; Wei Ye; Marios Savvides; Bhiksha Raj; Takahiro Shinozaki; Bernt Schiele; Jindong Wang; Xing Xie; Yue Zhang; |
| 211 | OpenSRH: Optimizing Brain Tumor Surgery Using Intraoperative Stimulated Raman Histology Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: OpenSRH is the first ever publicly available stimulated Raman histology (SRH) dataset and benchmark, which will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support. |
Cheng Jiang; Asadur Chowdury; Xinhai Hou; Akhil Kondepudi; Christian Freudiger; Kyle Conway; Sandra Camelo-Piragua; Daniel Orringer; Honglak Lee; Todd Hollon; |
| 212 | Turning The Tables: Biased, Dynamic, Imbalanced Tabular Datasets for ML Research Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, tabular data — which is prevalent in many high-stakes domains — has been lagging behind. To bridge this gap, we present Bank Account Fraud (BAF), the first publicly available 1 privacy-preserving, large-scale, realistic suite of tabular datasets. |
Sérgio Jesus; José Pombal; Duarte Alves; André Cruz; Pedro Saleiro; Rita Ribeiro; João Gama; Pedro Bizarro; |
| 213 | Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper thoroughly investigates the performance of 25 molecular design algorithms on 23 single-objective (scalar) optimization tasks with a particular focus on sample efficiency. |
Wenhao Gao; Tianfan Fu; Jimeng Sun; Connor Coley; |
| 214 | Video Compression Dataset and Benchmark of Learning-based Video-quality Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a new benchmark for video-quality metrics that evaluates video compression. |
Anastasia Antsiferova; Sergey Lavrushkin; Maksim Smirnov; Aleksandr Gushchin; Dmitriy Vatolin; Dmitriy Kulikov; |
| 215 | Flare7K: A Phenomenological Nighttime Flare Removal Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We design a new phenomenological synthetic flare dataset to help us remove the lens flare artifact at night. |
Yuekun Dai; Chongyi Li; Shangchen Zhou; Ruicheng Feng; Chen Change Loy; |
| 216 | GOOD: A Graph Out-of-Distribution Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we aim at developing an OOD benchmark, known as GOOD, for graphs specifically. |
Shurui Gui; Xiner Li; Limei Wang; Shuiwang Ji; |
| 217 | TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We release a new large-scale dataset for Chinese vision-language pretraining |
Yulong Liu; Guibo Zhu; Bin Zhu; Qi Song; Guojing Ge; Haoran Chen; GuanHui Qiao; Ru Peng; Lingxiang Wu; Jinqiao Wang; |
| 218 | VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: VLMbench is the first benchmark that compositional designs for vision-and-language reasoning and categorizes the manipulation tasks from the perspectives of task constraints. |
Kaizhi Zheng; Xiaotong Chen; Odest Chadwicke Jenkins; Xin Wang; |
| 219 | ViSioNS: Visual Search in Natural Scenes Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper builds a benchmark for comparing state-of-the-art human visual search models on different datasets comprising eye movements in natural scenes, discussing their limitations and how their integration could lead to performance improvements. |
Fermín Travi; Gonzalo Ruarte; Gaston Bujia; Juan Esteban Kamienkowski; |
| 220 | WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce WinoGAViL: an online game to collect vision-and-language associations, used as a dynamic benchmark to evaluate state-of-the-art models. |
Yonatan Bitton; Nitzan Bitton Guetta; Ron Yosef; Yuval Elovici; Mohit Bansal; Gabriel Stanovsky; Roy Schwartz; |
| 221 | Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Multi-LexSum is a multi-doc summarization dataset for civil rights litigations lawsuits with summaries of three granularities. |
Zejiang Shen; Kyle Lo; Lauren Yu; Nathan Dahlberg; Margo Schlanger; Doug Downey; |
| 222 | OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents two datasets of beautified faces — FairBeauty and B-LFW — and insights obtained through experiments; the datasets were created using a custom framework (OpenFilter). |
Piera Riccio; Bill Psomas; Francesco Galati; Francisco Escolano; Thomas Hofmann; Nuria Oliver; |
| 223 | Towards Video Text Visual Question Answering: Benchmark and Baseline Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a new task named Video Text Visual Question Answering (ViteVQA in short) that aims at answering questions by reasoning texts and visual information spatiotemporally in a given video. |
Minyi Zhao; Bingjia Li; Jie Wang; Wanqing Li; Wenjing Zhou; Lan Zhang; Shijie Xuyang; Zhihang Yu; Xinkun Yu; Guangze Li; Aobotao Dai; Shuigeng Zhou; |
| 224 | Model Zoos: A Dataset of Diverse Populations of Neural Network Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To enable the investigation of populations of neural network models, we release a novel dataset of diverse model zoos with this work. |
Konstantin Schürholt; Diyar Taskiran; Boris Knyazev; Xavier Giro-i-Nieto; Damian Borth; |
| 225 | Active-Passive SimStereo – Benchmarking The Cross-Generalization Capabilities of Deep Learning-based Stereo Methods Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the first dataset of active+passive stereo images to evaluate the generalisation ability of stereo deep learning models. |
Laurent Jospin; Allen Antony; Lian Xu; Hamid Laga; Farid Boussaid; Mohammed Bennamoun; |
| 226 | ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a dataset containing ten ensemble members over 20 years for post-processing ensemble weather forecasts. |
Saleh Ashkboos; Langwen Huang; Nikoli Dryden; Tal Ben-Nun; Peter Dueben; Lukas Gianinazzi; Luca Kummer; Torsten Hoefler; |
| 227 | NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We provide a benchmark for neural architecture search on a diverse set of understudied tasks. |
Renbo Tu; Nicholas Roberts; Misha Khodak; Junhong Shen; Frederic Sala; Ameet Talwalkar; |
| 228 | Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We empirically study the performance of GNNs with various synthetic graphs by synthetically changing one or a few target characteristic(s) of graphs while keeping other characteristics fixed. |
Seiji Maekawa; Koki Noda; Yuya Sasaki; makoto onizuka; |
| 229 | FlyView: A Bio-inspired Optical Flow Truth Dataset for Visual Navigation Using Panoramic Stereo Vision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we introduce FlyView, a novel bio-inspired truth dataset for visual navigation. |
Alix Leroy; Graham Taylor; |
| 230 | Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Though work to evaluate language model harms is under way, translating foresight about which harms may arise into rigorous benchmarks is not straightforward. To facilitate this translation, we outline six ways of characterizing harmful text which merit explicit consideration when designing new benchmarks. |
Maribeth Rauh; John Mellor; Jonathan Uesato; Po-Sen Huang; Johannes Welbl; Laura Weidinger; Sumanth Dathathri; Amelia Glaese; Geoffrey Irving; Iason Gabriel; William Isaac; Lisa Anne Hendricks; |
| 231 | Pythae: Unifying Generative Autoencoders in Python – A Benchmarking Use Case Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present Pythae, a versatile python library providing both a unified implementation and a dedicated framework allowing to perform straightforward reproducible and reliable use of generative autoencoder models. |
Clément Chadebec; Louis Vincent; Stephanie Allassonniere; |
| 232 | Robustness Analysis of Video-Language Models Against Visual and Language Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we perform the first extensive robustness study of video-language models against various real-world perturbations. |
Madeline Chantry; Shruti Vyas; Hamid Palangi; Yogesh Rawat; Vibhav Vineet; |
| 233 | A New Dataset for Multilingual Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While there are many recent papers on English keyphrase generation, keyphrase generation for other languages remains vastly understudied, mostly due to the absence of datasets. To address this, we present a novel dataset called Papyrus, composed of 16427 pairs of abstracts and keyphrases. |
Frédéric Piedboeuf; Philippe Langlais; |
| 234 | Benchmarking Heterogeneous Treatment Effect Models Through The Lens of Interpretability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We construct a benchmarking environment that allows us to empirically investigate the ability of personalized treatment effect models to identify predictive covariates. |
Jonathan Crabbé; Alicia Curth; Ioana Bica; Mihaela van der Schaar; |
| 235 | A Dataset for Efforts Towards Achieving The Sustainable Development Goal of Safe Working Environments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Consequently, we introduce a new dataset called the Labour Inspection Checklists Dataset (LICD), which we have made publicly available. |
Eirik Lund Flogard; Ole Jakob Mengshoel; |
| 236 | EgoTaskQA: Understanding Human Tasks in Egocentric Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present the EgoTaskQA benchmark that targets at action dependencies, post-effects, agents’ intents and goals, as well as multi-agent belief modeling in egocentric goal-oriented videos. |
Baoxiong Jia; Ting Lei; Song-Chun Zhu; Siyuan Huang; |
| 237 | Ontologue: Declarative Benchmark Construction for Ontological Multi-Label Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Ontologue is a toolkit for ontological multi-label classification dataset construction from DBPedia. This toolkit allows users to control contextual, distributional, and structured properties and create customized datasets. |
Sean Yang; Bernease Herman; Bill Howe; |
| 238 | BigBio: A Framework for Data-Centric Biomedical Natural Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: BigBio is a community library of 126+ biomedical NLP datasets, covering 13 tasks and 10 languages. |
Jason Fries; Leon Weber; Natasha Seelam; Gabriel Altay; Debajyoti Datta; Samuele Garda; Sunny Kang; Rosaline Su; Wojciech Kusa; Samuel Cahyawijaya; Fabio Barth; Simon Ott; Matthias Samwald; Stephen Bach; Stella Biderman; Mario Sänger; Bo Wang; Alison Callahan; Daniel León Periñán; Théo Gigant; Patrick Haller; Jenny Chim; Jose Posada; John Giorgi; Karthik Rangasai Sivaraman; Marc Pàmies; Marianna Nezhurina; Robert Martin; Michael Cullan; Moritz Freidank; Nathan Dahlberg; Shubhanshu Mishra; Shamik Bose; Nicholas Broad; Yanis Labrak; Shlok Deshmukh; Sid Kiblawi; Ayush Singh; Minh Chien Vu; Trishala Neeraj; Jonas Golde; Albert Villanova del Moral; Benjamin Beilharz; |
| 239 | Honor of Kings Arena: An Environment for Generalization in Competitive Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on the Honor of Kings, one of the world’s most popular games at present. |
Hua Wei; Jingxiao Chen; Xiyang Ji; Hongyang Qin; Minwen Deng; Siqin Li; Liang Wang; Weinan Zhang; Yong Yu; Liu Linc; Lanxiao Huang; Deheng Ye; Qiang Fu; Wei Yang; |
| 240 | SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the first unified platform SafeBench to effectively and efficiently evaluate autonomous driving algorithms against different types of safety-critical testing scenarios. |
Chejian Xu; Wenhao Ding; Weijie Lyu; ZUXIN LIU; Shuai Wang; Yihan He; Hanjiang Hu; DING ZHAO; Bo Li; |
| 241 | AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The existing benchmarks are focused on supervised learning, and to the best of our knowledge, there is none for unsupervised learning. Therefore, we introduce an unsupervised anomaly detection benchmark with data that shifts over time, built over Kyoto-2006+, a traffic dataset for network intrusion detection. |
Marius Dragoi; Elena Burceanu; Emanuela Haller; Andrei Manolache; Florin Brad; |
| 242 | Addressing Resource Scarcity Across Sign Languages with Multilingual Pretraining and Unified-Vocabulary Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We release the largest available pretraining dataset for sign language across multiple languages and show how multilingual fine-tuning using a unified vocabulary is helpful to achieve SOTA results |
Gokul NC; Manideep Ladi; Sumit Negi; Prem Selvaraj; Pratyush Kumar; Mitesh Khapra; |
| 243 | OpenXAI: Towards A Transparent Evaluation of Model Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce OpenXAI, a flexible and comprehensive open source ecosystem for evaluating, comparing, and benchmarking SOTA as well as any newly proposed explanation methods. |
Chirag Agarwal; Satyapriya Krishna; Eshika Saxena; Martin Pawelczyk; Nari Johnson; Isha Puri; Marinka Zitnik; Himabindu Lakkaraju; |
| 244 | MRI: Multi-modal 3D Human Pose Estimation Dataset Using MmWave, RGB-D, and Inertial Sensors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: mRI is a large-scale multi-modal human pose estimation dataset focusing on rehab movements, supporting human pose estimation and human activity recognition tasks. |
Sizhe An; Yin Li; Umit Ogras; |
| 245 | Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, finding such interesting and meaningful change events from the vast data is challenging. In this paper, we present new datasets for such change events that include semantically meaningful events like road construction. |
Utkarsh Mall; Bharath Hariharan; Kavita Bala; |
| 246 | PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The paper introduces a labeled tandem Mass Spectrometry dataset for machine learning in proteomics and recommends evaluation metrics. |
Omar Shouman; Wassim Gabriel; Victor-George Giurcoiu; Vitor Sternlicht; Mathias Wilhelm; |
| 247 | Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. |
guanghu yuan; Fajie Yuan; Yudong Li; Beibei Kong; Shujie Li; Lei Chen; Min Yang; Chenyun YU; Bo Hu; Zang Li; Yu Xu; Xiaohu Qie; |
| 248 | DC-BENCH: Dataset Condensation Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This work provides the first large-scale standardized benchmark on Dataset Condensation. |
Justin CUI; Ruochen Wang; Si Si; Cho-Jui Hsieh; |
| 249 | DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We extend the DABS benchmark, presenting improved datasets and algorithms for universal self-supervision |
Alex Tamkin; Gaurab Banerjee; Mohamed Owda; Vincent Liu; Shashank Rammoorthy; Noah Goodman; |
| 250 | The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We demonstrate PPO’s effectiveness in popular multi-agent benchmarks and analyze its properties and implementation details through empirical studies. |
Chao Yu; Akash Velu; Eugene Vinitsky; Jiaxuan Gao; Yu Wang; Alexandre Bayen; YI WU; |
| 251 | GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present the first multi-year mobile sensing datasets containing over 700 users to support the ML community in developing generalizable longitudinal behavior modeling algorithms |
Xuhai Xu; Han Zhang; Yasaman Sefidgar; Yiyi Ren; Xin Liu; Woosuk Seo; Jennifer Brown; Kevin Kuehn; Mike Merrill; Paula Nurius; Shwetak Patel; Tim Althoff; Margaret Morris; Eve Riskin; Jennifer Mankoff; Anind Dey; |
| 252 | ComMU: Dataset for Combinatorial Music Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose ComMU, a dataset for generating diverse and high-quality music with rich musical metadata. |
Lee Hyun; Taehyun Kim; Hyolim Kang; Minjoo Ki; Hyeonchan Hwang; kwanho park; Sharang Han; Seon Joo Kim; |
| 253 | SCAMPS: Synthetics for Camera Measurement of Physiological Signals Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: SCAMPS is a dataset of high-fidelity synthetics containing 2,800 videos (1.68M frames) of avatars with aligned cardiac and respiratory signals and facial action intensities. |
Daniel McDuff; Miah Wander; Xin Liu; Brian Hill; Javier Hernandez; Jonathan Lester; Tadas Baltrusaitis; |
| 254 | Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the Human-centric Analysis Toolkit (HAT), which enables evaluation of learned background bias without the need for new manual video annotation. |
Jihoon Chung; Yu Wu; Olga Russakovsky; |
| 255 | CGLB: Benchmark Tasks for Continual Graph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we systematically study the task configurations in different application scenarios and develop a comprehensive Continual Graph Learning Benchmark (CGLB) curated from different public datasets. |
Xikun Zhang; Dongjin Song; Dacheng Tao; |
| 256 | Towards Better Evaluation for Dynamic Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we proposed tools to improve evaluation of dynamic link prediction including new datasets, new negative sampling strategies, and a strong baseline. |
Farimah Poursafaei; Shenyang Huang; Kellin Pelrine; Reihaneh Rabbany; |
| 257 | AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We use open source 3D movies to make a new 2D animation dataset with ground truth optical flow and segment-wise correspondence label. |
Li Siyao; Yuhang Li; Bo Li; Chao Dong; Ziwei Liu; Chen Change Loy; |
| 258 | FLAIR: Federated Learning Annotated Image Repository Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper describes the FLAIR dataset that we are releasing later this month to accelerate research in Federated Learning. This is a large image dataset that is heterogenous, with images grouped by Flicker users and annotated by human. |
Congzheng Song; Filip Granqvist; Kunal Talwar; |
| 259 | LAION-5B: An Open Large-scale Dataset for Training Next Generation Image-text Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present LAION-5B, an open, publically available dataset of 5.8B image-text pairs and validate it by reproducing results of training state-of-the-art CLIP models of different scale. |
Christoph Schuhmann; Romain Beaumont; Richard Vencu; Cade Gordon; Ross Wightman; Mehdi Cherti; Theo Coombes; Aarush Katta; Clayton Mullis; Mitchell Wortsman; Patrick Schramowski; Srivatsa Kundurthy; Katherine Crowson; Ludwig Schmidt; Robert Kaczmarczyk; Jenia Jitsev; |
| 260 | OpenOOD: Benchmarking Generalized Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We build an open-source codebase called OpenOOD to support and compare 30+ methods for OOD detection and beyond. |
Jingkang Yang; Pengyun Wang; Dejian Zou; Zitang Zhou; Kunyuan Ding; WENXUAN PENG; Haoqi Wang; Guangyao Chen; Bo Li; Yiyou Sun; Xuefeng Du; Kaiyang Zhou; Wayne Zhang; Dan Hendrycks; Yixuan Li; Ziwei Liu; |
| 261 | Nocturne: A Scalable Driving Benchmark for Bringing Multi-agent Learning One Step Closer to The Real World Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a fast, data-driven simulator for studying multi-agent partially observed coordination in human driving. |
Eugene Vinitsky; Nathan Lichtlé; Xiaomeng Yang; Brandon Amos; Jakob Foerster; |
| 262 | PDEBench: An Extensive Benchmark for Scientific Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We provide a benckmark for Scientific Machine Learning |
Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert; |
| 263 | ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: ELEVATER provides the first public platform and toolkit to evaluate vision foundation models in their large-scale task-level visual transfer in 20 image classification tasks and 35 object detection tasks |
Chunyuan Li; Haotian Liu; Liunian Li; Pengchuan Zhang; Jyoti Aneja; Jianwei Yang; Ping Jin; Houdong Hu; Zicheng Liu; Yong Jae Lee; Jianfeng Gao; |
| 264 | Open High-Resolution Satellite Imagery: The WorldStrat Dataset – With Application to Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStratified dataset. |
Julien Cornebise; Ivan Oršolić; Freddie Kalaitzis; |
| 265 | EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we aim to address a common bottleneck in the RL training system, i.e., parallel environment execution, which is often the slowest part of the whole system but receives little attention. |
Jiayi Weng; Min Lin; Shengyi Huang; Bo Liu; Denys Makoviichuk; Viktor Makoviychuk; Zichen Liu; Yufan Song; Ting Luo; Yukun Jiang; Zhongwen Xu; Shuicheng Yan; |
| 266 | Dungeons and Data: A Large-Scale NetHack Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce and evaluate a new large-scale dataset for the game of NetHack, including 10 billion transitions from humans, 3 billion from a symbolic bot, and code for researchers to record and load their own trajectories. |
Eric Hambro; Roberta Raileanu; Danielle Rothermel; Vegard Mella; Tim Rocktäschel; Heinrich Küttler; Naila Murray; |
| 267 | EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a new text-to-SQL dataset for electronic health records (EHRs), where the utterances are collected from 222 hospital staff—including physicians, nurses, and insurance review and health records teams through a poll conducted at a university hospital. |
GYUBOK LEE; Hyeonji Hwang; Seongsu Bae; Yeonsu Kwon; Woncheol Shin; Seongjun Yang; Minjoon Seo; Jong-Yeup Kim; Edward Choi; |
| 268 | TempEL: Linking Dynamically Evolving and Newly Emerging Entities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We study how this evolutionary scenario impacts the performance on a well established entity linking (EL) task. For that study, we introduce TempEL, an entity linking dataset that consists of time-stratified English Wikipedia snapshots from 2013 to 2022, from which we collect both anchor mentions of entities, and these target entities’ descriptions. |
Klim Zaporojets; Lucie-Aimée Kaffee; Johannes Deleu; Thomas Demeester; Chris Develder; Isabelle Augenstein; |
| 269 | FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the FinRL community. |
Xiao-Yang Liu; Ziyi Xia; Jingyang Rui; Jiechao Gao; Hongyang Yang; Ming Zhu; Christina Wang; Zhaoran Wang; Jian Guo; |
| 270 | MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We release a dataset of experts and their rollouts for tracking 3.5 hours of MoCap data in dm_control. |
Nolan Wagener; Andrey Kolobov; Felipe Vieira Frujeri; Ricky Loynd; Ching-An Cheng; Matthew Hausknecht; |
| 271 | OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender Within Occupations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present the OccGen toolkit that builds multilingual parallel data sets balanced in gender within occupations. The toolkit is released together with two datasets in four high-resource languages and in a low-resource language (with English). |
Marta Costa-jussà; Christine Basta; Oriol Domingo; André Rubungo; |
| 272 | The Dollar Street Dataset: Images Representing The Geographic and Socioeconomic Diversity of The World Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present Dollar Street, a supervised dataset that contains 38,479 images of everyday household items from homes around the world, including tags for objects and demographic data such as region, country and home monthly income. |
William Gaviria Rojas; Sudnya Diamos; Keertan Kini; David Kanter; Vijay Janapa Reddi; Cody Coleman; |
| 273 | NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: NeoRL presents conservative datasets for offline RL, highlights the complete pipeline for deploying offline RL in real-world applications, and also benchmarks recent offline RL algorithms on NeoRL under the complete pipeline. |
Rong-Jun Qin; Xingyuan Zhang; Songyi Gao; Xiong-Hui Chen; Zewen Li; Weinan Zhang; Yang Yu; |
| 274 | PeRFception: Perception Using Radiance Fields Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a new dataset, PeRFception dataset, that is a new unified radiance field dataset for the 2D image classification, 3D shape classification, and 3D semantic segmentation. |
Yoonwoo Jeong; Seungjoo Shin; Junha Lee; Chris Choy; Anima Anandkumar; Minsu Cho; Jaesik Park; |
| 275 | PyKT: A Python Library to Benchmark Deep Learning Based Knowledge Tracing Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a comprehensive python based benchmark platform, pyKT, to guarantee valid comparisons across deep learning based knowledge tracing methods via thorough evaluations. |
Zitao Liu; Qiongqiong Liu; Jiahao Chen; Shuyan Huang; Jiliang Tang; Weiqi Luo; |
| 276 | TweetNERD – End to End Entity Linking Benchmark for Tweets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: TweetNERD is a dataset of 340K+ Tweets for benchmarking Named Entity Recognition and Disambiguation systems on English Tweets. |
Shubhanshu Mishra; Aman Saini; Raheleh Makki; Sneha Mehta; Aria Haghighi; Ali Mollahosseini; |
| 277 | Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. |
Ihsan Ullah; Dustin Carrión-Ojeda; Sergio Escalera; Isabelle Guyon; Mike Huisman; Felix Mohr; Jan N. van Rijn; Haozhe Sun; Joaquin Vanschoren; Phan Anh Vu; |
| 278 | OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an open-source platform for Full Waveform Inversion with twelve datasets and benchmarks on four deep learning methods. |
Chengyuan Deng; Shihang Feng; Hanchen Wang; Xitong Zhang; Peng Jin; Yinan Feng; Qili Zeng; Yinpeng Chen; Youzuo Lin; |
| 279 | OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a first-of-its-kind dataset that combines clinical labels, biomarkers, fundus, OCT scans, for disease prediction, treatment analysis and biomarker detection. |
Mohit Prabhushankar; Kiran Kokilepersaud; Yash-yee Logan; Stephanie Trejo Corona; Ghassan AlRegib; Charles Wykoff; |
| 280 | MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. |
Jorge Quesada; Lakshmi Sathidevi; Ran Liu; Nauman Ahad; Joy Jackson; Mehdi Azabou; Jingyun Xiao; Christopher Liding; Matthew Jin; Carolina Urzay; William Gray-Roncal; Erik Johnson; Eva Dyer; |
| 281 | K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce KAIST-Radar (K-Radar), a novel large-scale object detection dataset and benchmark that contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions, together with carefully annotated 3D bounding box labels of objects on the roads. |
Dong-Hee Paek; SEUNG-HYUN KONG; Kevin Tirta Wijaya; |
| 282 | Multilingual Abusive Comment Detection at Scale for Indic Languages Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To facilitate and encourage research in this important direction, we contribute for the first time MACD – a large-scale (150K), human-annotated, multilingual (5 languages), balanced (49\% abusive content) and diverse (70K users) abuse detection dataset of user comments, sourced from a popular social media platform – ShareChat. |
Vikram Gupta; Sumegh Roychowdhury; Mithun Das; Somnath Banerjee; Punyajoy Saha; Binny Mathew; hastagiri prakash vanchinathan; Animesh Mukherjee; |
| 283 | Geoclidean: Few-Shot Generalization in Euclidean Geometry Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce Geoclidean, a domain-specific language for Euclidean geometry, and use it to generate two datasets of geometric concept learning tasks for benchmarking generalization judgements of humans and machines. |
Joy Hsu; Jiajun Wu; Noah Goodman; |
| 284 | HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We construct and analyze a large-scale longitudinal dataset of commercial ML API predictions. |
Lingjiao Chen; Zhihua Jin; Evan Sabri Eyuboglu; Christopher Ré; Matei Zaharia; James Zou; |
| 285 | DART: Articulated Hand Model with Diverse Accessories and Rich Textures Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present DART, which extends MANO with diverse accessories and rich textures, and synthesize a large-scale (800K) hand dataset. |
Daiheng Gao; Yuliang Xiu; Kailin Li; Lixin Yang; Feng Wang; Peng Zhang; Bang Zhang; Cewu Lu; Ping Tan; |
| 286 | DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper present DGraph, a real-world dynamic graph in the finance domain. |
Xuanwen Huang; Yang Yang; Yang Wang; Chunping Wang; Zhisheng Zhang; Jiarong Xu; Lei Chen; Michalis Vazirgiannis; |
| 287 | PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: PulseImpute is the first mHealth pulsative signal imputation challenge which includes realistic missingness models, clinical downstream tasks, and an extensive set of baselines, including an augmented transformer that achieves SOTA performance. |
Maxwell Xu; Alexander Moreno; Supriya Nagesh; Varol Aydemir; David Wetter; Santosh Kumar; James Rehg; |
| 288 | On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with No Catastrophic Forgetting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We describe and exploit connections between two distinct paradigms for expressing preferences over outputs of language models: reward maximization and distribution matching. |
Tomasz Korbak; Hady Elsahar; Germán Kruszewski; Marc Dymetman; |
| 289 | On The Strong Correlation Between Model Invariance and Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Building on this qualitative implication we make two contributions. First, we introduce effective invariance (EI), a simple and reasonable measure of model invariance which does not rely on image labels. Given predictions on a test image and its transformed version, EI measures how well the predictions agree and with what level of confidence. Second, using invariance scores computed by EI, we perform large-scale quantitative correlation studies between generalization and invariance, focusing on rotation and grayscale transformations. |
Weijian Deng; Stephen Gould; Liang Zheng; |
| 290 | Adaptive Interest for Emphatic Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a way to automatically learn the interest function of emphatic algorithms and verify our approach on a wide range of environments. |
Martin Klissarov; Rasool Fakoor; Jonas Mueller; Kavosh Asadi; Taesup Kim; Alexander Smola; |
| 291 | Hilbert Distillation for Cross-Dimensionality Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel Hilbert curve-based cross-dimensionality distillation approach that facilitates the knowledge of 3D networks to improve the performance of 2D networks. |
Dian Qin; Haishuai Wang; Zhe Liu; HONGJIA XU; Sheng Zhou; Jiajun Bu; |
| 292 | Distributionally Adaptive Meta Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop a framework for meta-RL algorithms that are able to behave appropriately under test-time distribution shifts in the space of tasks. |
Anurag Ajay; Dibya Ghosh; Sergey Levine; Pulkit Agrawal; Abhishek Gupta; |
| 293 | Simplified Graph Convolution with Heterophily Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a simple, non-deep method for graph convolution which can handle both homophilous and heterophilous graphs. |
Sudhanshu Chanpuriya; Cameron Musco; |
| 294 | Online Allocation and Learning in The Presence of Strategic Agents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of sequentially allocating items to n potentially strategic agents with unknown prior on their value distribution. |
Steven Yin; Shipra Agrawal; Assaf Zeevi; |
| 295 | Accelerating Certified Robustness Training Via Knowledge Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose Certified Robustness Transfer (CRT), a general-purpose framework for reducing the computational overhead of any certifiably robust training method through knowledge transfer. |
Pratik Vaishnavi; Kevin Eykholt; Amir Rahmati; |
| 296 | Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We show that one can beat the exponential computation-statistical gap for worst-case function classes in smooth online learning when one considers generalized linear function classes. |
Adam Block; Max Simchowitz; |
| 297 | ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We recursively generate 3D shape distributions from progressively evolving phrase sequences. |
Rao Fu; Xiao Zhan; YIWEN CHEN; Daniel Ritchie; Srinath Sridhar; |
| 298 | Trajectory Inference Via Mean-field Langevin in Path Space Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The estimator for trajectory inference that minimizes the entropy relative to Wiener measure can be computed with a Langevin dynamics in path space (convergence guaranteed). |
Stephen Zhang; Lénaïc Chizat; Matthieu Heitz; Geoffrey Schiebinger; |
| 299 | Beyond Black Box Densities: Parameter Learning for The Deviated Components Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the "deviating mixture model" and study its theoretical properties. |
Dat Do; Nhat Ho; XuanLong Nguyen; |
| 300 | Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an oracle-efficient algorithm for boosting robustness to adversarial examples. |
Avrim Blum; Omar Montasser; Greg Shakhnarovich; Hongyang Zhang; |
| 301 | Optimal Efficiency-Envy Trade-Off Via Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We use tools from Optimal Transport to achieve optimal trade-off between efficiency and envy in resource allocation problems. |
Steven Yin; Christian Kroer; |
| 302 | One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a method named Free-Form CLIP (FFCLIP), aiming to establish an automatic latent mapping so that one manipulation model handles free-form text prompts. |
Yiming Zhu; Hongyu Liu; Yibing Song; Ziyang Yuan; Xintong Han; Chun Yuan; Qifeng Chen; Jue Wang; |
| 303 | (De-)Randomized Smoothing for Decision Stump Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a (De-)Randomized Smoothing approach for decision stump ensembles, which i) significantly improves SOTA certified Lp-norm robustness for tree-based models and ii) enables joint certificates of numerical & categorical perturbations. |
Miklós Horváth; Mark Müller; Marc Fischer; Martin Vechev; |
| 304 | Generative Multitask Learning Mitigates Target-causing Confounding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We use ideas from causality to develop an inference objective for MTL that improves robustness to target shift. |
Taro Makino; Krzysztof Geras; Kyunghyun Cho; |
| 305 | IM-Loss: Information Maximization Loss for Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the forward-passing $0/1$ spike quantization will cause information loss and accuracy degradation. To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper. |
Yufei Guo; Yuanpei Chen; Liwen Zhang; Xiaode Liu; Yinglei Wang; Xuhui Huang; Zhe Ma; |
| 306 | Low-Rank Modular Reinforcement Learning Via Muscle Synergy Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Motivated by the way the human central nervous system controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control. |
Heng Dong; Tonghan Wang; Chongjie Zhang; |
| 307 | A Differentially Private Linear-Time FPTAS for The Minimum Enclosing Ball Problem Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this works, we give the first differentially private (DP) fPTAS for the Minimum Enclosing Ball problem, improving both on the runtime and the utility bound of the best known DP-PTAS for the problem, of Ghazi et al (2020). |
Bar Mahpud; Or Sheffet; |
| 308 | Optimal Query Complexities for Dynamic Trace Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We give tight bounds for implicity trace estimation in a dynamic setting. |
David Woodruff; Fred Zhang; Richard Zhang; |
| 309 | GALOIS: Boosting Deep Reinforcement Learning Via Generalizable Logic Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on that, GALOIS proposes a sketch-based program synthesis method to automatically generate white-box programs with generalizable and interpretable cause-effect logic. |
Yushi Cao; Zhiming Li; Tianpei Yang; Hao Zhang; YAN ZHENG; Yi Li; Jianye Hao; Yang Liu; |
| 310 | Near-Optimal Sample Complexity Bounds for Constrained MDPs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide minimax sample-complexity bounds for learning near-optimal policies for discounted constrained Markov decision processes (CMDPs) with access to a simulator.. |
Sharan Vaswani; Lin Yang; Csaba Szepesvari; |
| 311 | ReFactorGNNs: Revisiting Factorisation-based Models from A Message-Passing Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose ReFactor GNNs inspired by revisiting FMs from the perspective of message-passing. |
Yihong Chen; Pushkar Mishra; Luca Franceschi; Pasquale Minervini; Pontus Lars Erik Saito Stenetorp; Sebastian Riedel; |
| 312 | When Adversarial Training Meets Vision Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper investigates the training techniques and utilizes the unique architectures to improve the adversarial robustness of Vision transformers. |
Yichuan Mo; Dongxian Wu; Yifei Wang; Yiwen Guo; Yisen Wang; |
| 313 | Interventions, Where and How? Bayesian Active Causal Discovery at Scale Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we incorporate recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, which allows for active causal discovery of nonlinear, large SCMs, while selecting both the target and the value to intervene with. |
Panagiotis Tigas; Yashas Annadani; Andrew Jesson; Bernhard Schölkopf; Yarin Gal; Stefan Bauer; |
| 314 | Nonlinear MCMC for Bayesian Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We provide a convergence guarantee in total variation that uses novel results for long-time convergence and large-particle (“propagation of chaos”) convergence. We apply this nonlinear MCMC technique to sampling problems including a Bayesian neural network on CIFAR10. |
James Vuckovic; |
| 315 | Robust Neural Posterior Estimation and Statistical Model Criticism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a remedy we argue that principled scientific inquiry with simulators should incorporate a model criticism component, to facilitate interpretable identification of misspecification and a robust inference component, to fit "wrong but useful" models. We propose robust neural posterior estimation (RNPE), an extension of NPE to simultaneously achieve both these aims, through explicitly modelling the discrepancies between simulations and the observed data. |
Daniel Ward; Patrick Cannon; Mark Beaumont; Matteo Fasiolo; Sebastian Schmon; |
| 316 | Multi-layer State Evolution Under Random Convolutional Design Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show how to deal with Convolutional Matrices with Approximate Message Passing |
Max Daniels; Cedric Gerbelot; Florent Krzakala; Lenka Zdeborová; |
| 317 | Unsupervised Object Representation Learning Using Translation and Rotation Group Equivariant VAE Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a translation and rotation group equivariant variational autoencoder by performing direct inference on these transformations. |
Alireza Nasiri; Tristan Bepler; |
| 318 | Estimation of Entropy in Constant Space with Improved Sample Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we give a new constant memory scheme that reduces the sample complexity to $(k/\epsilon^2)\cdot \text{polylog}(1/\epsilon)$. |
Maryam Aliakbarpour; Andrew McGregor; Jelani Nelson; Erik Waingarten; |
| 319 | Grounding Aleatoric Uncertainty in Unsupervised Environment Design Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We characterize how curriculum learning can induce suboptimal reinforcement learning policies with respect to a ground-truth distribution of environments, and propose a method for correcting this effect. |
Minqi Jiang; Michael Dennis; Jack Parker-Holder; Andrei Lupu; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel; Jakob Foerster; |
| 320 | A Deep Learning Toolbox for Stochastic Stabilized Supralinear Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a method to train biologically realistic stabilized supralinear networks in a stable manner. |
Wayne Soo; Mate Lengyel; |
| 321 | Batch Bayesian Optimization on Permutations Using The Acquisition Weighted Kernel Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives. |
Changyong Oh; Roberto Bondesan; Efstratios Gavves; Max Welling; |
| 322 | Aligning Individual Brains with Fused Unbalanced Gromov Wasserstein Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We derive a new unbalanced optimal transport loss to align human individual brains using fMRI data while preserving their anatomical topology |
Alexis Thual; Quang Huy TRAN; Tatiana Zemskova; Nicolas Courty; Rémi Flamary; Stanislas Dehaene; Bertrand Thirion; |
| 323 | [Re] Does Self-Supervision Always Improve Few-Shot Learning? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Scope of Reproducibility: This report covers our reproduction and extension of the paper ‘When Does Self-Supervision Improve Few-shot Learning?’ |
Arjun Ashok; Haswanth Aekula; |
| 324 | Sequential Latent Variable Models for Multiagent Trajectories Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a semi-supervised generative framework for modeling and annotating trajectories of multiple agents. |
Dennis Fassmeyer; Pascal Fassmeyer; Ulf Brefeld; |
| 325 | Bayesian Inference Via Sparse Hamiltonian Flows Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper shows how to (1) construct Bayesian coresets simply and tractably using variational flows, and (2) make variational flows cheaper in the large-data regime via coresets. |
Naitong Chen; Zuheng Xu; Trevor Campbell; |
| 326 | Learning with Convolution and Pooling Operations in Kernel Methods Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We describe the generalization properties of a one-layer convolutional kernel with pooling and downsampling. |
Theodor Misiakiewicz; Song Mei; |
| 327 | Mean Estimation with User-level Privacy Under Data Heterogeneity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study mean estimation in the setting with users with heterogeneous data. |
Rachel Cummings; Vitaly Feldman; Audra McMillan; Kunal Talwar; |
| 328 | On The Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we provide an efficient algorithm to compute a universal plug-in estimator for symmetric properties of distributions that is sample optimal up to accuracy $\epsilon \gg n^{-1/3}$, where $n$ is the sample size. |
Moses Charikar; Zhihao Jiang; Kirankumar Shiragur; Aaron Sidford; |
| 329 | Data Augmentation for Compositional Data: Advancing Predictive Models of The Microbiome Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose novel data augmentation strategies that yield significant performance gains for microbiome compositional data. |
Elliott Gordon-Rodriguez; Thomas Quinn; John Cunningham; |
| 330 | Learning in Distributed Contextual Linear Bandits Without Sharing The Context Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a method to compress the context using $\approx 5d$ bits per context if the context distribution is unknown and $0$ bits per context if the context distribution is known, while achieving optimal regret. |
Osama Hanna; Lin Yang; Christina Fragouli; |
| 331 | Parameter-free Dynamic Graph Embedding for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, this paper proposes FreeGEM, a parameter-free dynamic graph embedding method for link prediction. |
Jiahao Liu; Dongsheng Li; Hansu Gu; Tun Lu; Peng Zhang; Ning Gu; |
| 332 | Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show that tree-based methods are surprisingly strong baselines for subgroup robustness on tabular data. |
Josh Gardner; Zoran Popovic; Ludwig Schmidt; |
| 333 | Characterizing The Ventral Visual Stream with Response-Optimized Neural Encoding Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a data-driven, hypothesis-agnostic computational approach to understand representations within the human ventral visual pathway |
Meenakshi Khosla; Keith Jamison; Amy Kuceyeski; Mert Sabuncu; |
| 334 | Multi-Class $H$-Consistency Bounds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an extensive study of $H$-consistency bounds formulti-class classification. |
Pranjal Awasthi; Anqi Mao; Mehryar Mohri; Yutao Zhong; |
| 335 | Learning Bipartite Graphs: Heavy Tails and Multiple Components Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose estimators for (k-component) bipartite graphs under the assumption that the observed data is heavy-tailed. |
José Vinícius de Miranda Cardoso; Jiaxi Ying; Daniel Palomar; |
| 336 | Autoregressive Perturbations for Data Poisoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce autoregressive (AR) poisoning, a method that can generate poisoned data without access to the broader dataset. |
Pedro Sandoval-Segura; Vasu Singla; Jonas Geiping; Micah Goldblum; Tom Goldstein; David Jacobs; |
| 337 | Does GNN Pretraining Help Molecular Representation? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate graph pretraining on molecular representation. We conduct thorough ablation studies on the key components of GNN pretraining, and found that many occasions the benefits from self-supervised pretraining on molecular data is negligible. |
Ruoxi Sun; Hanjun Dai; Adams Yu; |
| 338 | Discrete Compositional Representations As An Abstraction for Goal Conditioned Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Defining goals in the space of noisy, high-dimensional sensory inputs is one possibility, yet this poses a challenge for training goal-conditioned agents, or even for generalization to novel goals. We propose to address this by learning compositional representations of goals and processing the resulting representation via a discretization bottleneck, for coarser specification of goals, through an approach we call DGRL. |
Riashat Islam; Hongyu Zang; Anirudh Goyal; Alex Lamb; Kenji Kawaguchi; Xin Li; Romain Laroche; Yoshua Bengio; Remi Tachet des Combes; |
| 339 | E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose E-MAPP, a framework for parallel program guided multi-agent reinforcement learning, which outperforms strong baselines in long-horizon cooperation tasks and generalizes well. |
Can Chang; Ni Mu; Jiajun Wu; Ling Pan; Huazhe Xu; |
| 340 | Unsupervised Learning of Shape Programs with Repeatable Implicit Parts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a shape ProGram with Repeatable Implicit Parts (ProGRIP) along with an unsupervised learning strategy that helps learn high fidelity structured shape with self-similarity considered. |
Boyang Deng; Sumith Kulal; Zhengyang Dong; Congyue Deng; Yonglong Tian; Jiajun Wu; |
| 341 | Towards Hard-pose Virtual Try-on Via 3D-aware Global Correspondence Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we target image-based person-to-person virtual try-on in the presence of diverse poses and large viewpoint variations. |
Zaiyu Huang; Hanhui Li; Zhenyu Xie; Michael Kampffmeyer; qingling Cai; Xiaodan Liang; |
| 342 | A Fourier Approach to Mixture Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We give a simple algorithm that learns spherical Gaussian mixtures with a nearly-optimal separation in the moderate-dimension regime. |
Mingda Qiao; Guru Guruganesh; Ankit Rawat; Kumar Avinava Dubey; Manzil Zaheer; |
| 343 | The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a diversity control regularization term to accommodate the multi-vector representation strategy better. |
Shilong Bao; Qianqian Xu; Zhiyong Yang; Yuan He; Xiaochun Cao; Qingming Huang; |
| 344 | Debiased Machine Learning Without Sample-Splitting for Stable Estimators Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We prove asymptotic normality for a target parameter of interest, of debiased machine learning semi-parametric estimators without sample splitting, when the machine learning estimators used for the nuisance functions are leave-one-out stable. |
Qizhao Chen; Vasilis Syrgkanis; Morgane Austern; |
| 345 | Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose DecSPS, a novel variant of stochastic Polyak stepsize (SPS) for SGD, yielding first stochastic *adaptive* optimization method that converges to exact solution without restrictive assumptions like bounded iterates/gradients or interpolation |
Antonio Orvieto; Simon Lacoste-Julien; Nicolas Loizou; |
| 346 | Generalization Error Bounds on Deep Learning with Markov Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we derive upper bounds on generalization errors for deep neural networks with Markov datasets. |
Lan V. Truong; |
| 347 | Differentially Private Graph Learning Via Sensitivity-Bounded Personalized PageRank Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide the first differential private algorithm for approximating personalized page rank. |
Alessandro Epasto; Vahab Mirrokni; Bryan Perozzi; Anton Tsitsulin; Peilin Zhong; |
| 348 | Online Agnostic Multiclass Boosting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We give the first boosting algorithm for online agnostic multiclass classification by reducing boosting to online convex optimization. |
Vinod Raman; Ambuj Tewari; |
| 349 | Data-Efficient Structured Pruning Via Submodular Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a principled data-efficient structured pruning method based on submodular optimization. |
Marwa El Halabi; Suraj Srinivas; Simon Lacoste-Julien; |
| 350 | Masked Prediction: A Parameter Identifiability View Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work offers a new lens to understanding self-supervised learning: one of parameter identifiability. We show that with proper choices of parametric forms and prediction tasks, masked prediction tasks can recover parameters of HMMs. |
Bingbin Liu; Daniel Hsu; Pradeep Ravikumar; Andrej Risteski; |
| 351 | A Unified Analysis of Federated Learning with Arbitrary Client Participation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a unified framework for analyzing the convergence of federated learning with arbitrary participation of clients. |
Shiqiang Wang; Mingyue Ji; |
| 352 | When Does Return-conditioned Supervised Learning Work for Offline Reinforcement Learning? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we provide a rigorous study of the capabilities and limitations of RCSL something which is crucially missing in previous work. |
David Brandfonbrener; Alberto Bietti; Jacob Buckman; Romain Laroche; Joan Bruna; |
| 353 | Near-Optimal Private and Scalable $k$-Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide nearly optimal algorithms for differentially private k-means and k-median clustering in Euclidean space, in the massively parallel computation model. |
Vincent Cohen-Addad; Alessandro Epasto; Vahab Mirrokni; Shyam Narayanan; Peilin Zhong; |
| 354 | Decision-Focused Learning Without Decision-Making: Learning Locally Optimized Decision Losses Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide a novel way to learn loss functions for predictive models so as to improve their performance when used in conjunction with specific optimization tasks. |
Sanket Shah; Kai Wang; Bryan Wilder; Andrew Perrault; Milind Tambe; |
| 355 | PAC Prediction Sets for Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel algorithm to construct a prediction set for meta learning that satisfies a probably approximately correct (PAC) guarantee tailored to meta learning. |
Sangdon Park; Edgar Dobriban; Insup Lee; Osbert Bastani; |
| 356 | Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We use policy optimization with advantage regularization to improve long-term fairness of decision-making policies. |
Eric Yu; Zhizhen Qin; Min Kyung Lee; Sicun Gao; |
| 357 | Layer Freezing & Data Sieving: Missing Pieces of A Generic Framework for Sparse Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper intends to explore other possible directions to effectively and efficiently reduce sparse training costs while preserving accuracy. |
Geng Yuan; Yanyu Li; Sheng Li; Zhenglun Kong; Sergey Tulyakov; Xulong Tang; Yanzhi Wang; Jian Ren; |
| 358 | Rapid Model Architecture Adaption for Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The combinatorial search complexity $T \times H$ creates a fundamental search efficiency challenge if one naively applies existing NAS methods to these scenarios. To overcome this issue, we show, for the first time, how to rapidly adapt model architectures to new tasks in a \emph{many-task many-hardware} few-shot learning setup by integrating Model Agnostic Meta Learning (MAML) into the NAS flow. |
Yiren Zhao; Xitong Gao; I Shumailov; Nicolo Fusi; Robert Mullins; |
| 359 | Compositional Generalization Through Abstract Representations in Human and Artificial Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the impact of abstract representations on compositional generalization in human imaging data and simple artificial neural networks. |
Takuya Ito; Tim Klinger; Doug Schultz; John Murray; Michael Cole; Mattia Rigotti; |
| 360 | Surprising Instabilities in Training Deep Networks and A Theoretical Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We empirically demonstrate numerical instabilities in training deep networks with SGD and provide a theoretical analysis for it. |
Yuxin Sun; DONG LAO; Ganesh Sundaramoorthi; Anthony Yezzi; |
| 361 | Change-point Detection for Sparse and Dense Functional Data in General Dimensions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We study the problem of change-point detection and localisation for functional data sequentially observed on a general $d$-dimensional space, where we allow the functional curves to be either sparsely or densely sampled. |
Carlos Misael Madrid Padilla; Daren Wang; Zifeng Zhao; Yi Yu; |
| 362 | Mesoscopic Modeling of Hidden Spiking Neurons Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We derive a neuronally-grounded latent variable model for multi-neuronal spike trains. |
Shuqi Wang; Valentin Schmutz; Guillaume Bellec; Wulfram Gerstner; |
| 363 | A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of online learning with feedback graphs and present an algorithm capable of achieving near-optimal pseudo-regret bounds simultaneously against adversarial and stochastic sequences of losses. |
Chloé Rouyer; Dirk van der Hoeven; Nicolò Cesa-Bianchi; Yevgeny Seldin; |
| 364 | Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new method for approximating active learning acquisition strategies that are based on retraining with hypothetically-labeled candidate data points. |
Mohamad Amin Mohamadi; Wonho Bae; Danica J. Sutherland; |
| 365 | Graph Neural Networks Are Dynamic Programmers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We use category theory and abstract algebra to further uncover the relationship between graph neural nets and dynamic programming, which was previously done handwavily over specific examples. |
Andrew J Dudzik; Petar Veličković; |
| 366 | Generalized Laplacian Eigenmaps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose GLEN, an NP-hard rank difference minimization problem for graph node embedding that enjoys the intra-class separation guarantee and can be solved with a logdet relaxation. |
Hao Zhu; Piotr Koniusz; |
| 367 | Invertible Monotone Operators for Normalizing Flows Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a monotone operator-based normalizing flow by parametrizing the Cayley operator of monotone operators. |
Byeongkeun Ahn; Chiyoon Kim; Youngjoon Hong; Hyunwoo Kim; |
| 368 | FR: Folded Rationalization with A Unified Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, such a two-phase model may incur the degeneration problem where the predictor overfits to the noise generated by a not yet well-trained generator and in turn, leads the generator to converge to a suboptimal model that tends to select senseless pieces. To tackle this challenge, we propose Folded Rationalization (FR) that folds the two phases of the rationale model into one from the perspective of text semantic extraction. |
Wei Liu; Haozhao Wang; Jun Wang; Ruixuan Li; Chao Yue; YuanKai Zhang; |
| 369 | Riemannian Diffusion Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a continuous-time diffusion model for data represented on a Riemannian manifold. |
Chin-Wei Huang; Milad Aghajohari; Joey Bose; Prakash Panangaden; Aaron Courville; |
| 370 | Training with More Confidence: Mitigating Injected and Natural Backdoors During Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: By further analyzing the training process and model architectures, we found that piece-wise linear functions cause this hyperplane surface. In this paper, we design a novel training method that forces the training to avoid generating such hyperplanes and thus remove the injected backdoors. |
Zhenting Wang; Hailun Ding; Juan Zhai; Shiqing Ma; |
| 371 | GhostNetV2: Enhance Cheap Operation with Long-Range Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a hardware-friendly attention mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications. |
Yehui Tang; Kai Han; Jianyuan Guo; Chang Xu; Chao Xu; Yunhe Wang; |
| 372 | Chefs’ Random Tables: Non-Trigonometric Random Features Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a new family of random features for the Gaussian kernel. Extensive theoretical and empirical analysis is presented. |
Valerii Likhosherstov; Krzysztof M Choromanski; Kumar Avinava Dubey; Frederick Liu; Tamas Sarlos; Adrian Weller; |
| 373 | Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We give a new efficient approximate parallel algorithm for graph-based average-linkage HAC which is scalable and high quality relative to existing state-of-the-art hierarchical clustering algorithms. |
Laxman Dhulipala; David Eisenstat; Jakub Lacki; Vahab Mirrokni; Jessica Shi; |
| 374 | Efficient Non-Parametric Optimizer Search for Diverse Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: With the goal of democratizing research and application of optimizer search, we present the first efficient, scalable and generalizable framework that can directly search on the tasks of interest. |
Ruochen Wang; Yuanhao Xiong; Minhao Cheng; Cho-Jui Hsieh; |
| 375 | Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our algorithm Factored-DRO learns distributionally robust batch contextual bandit policies, and can separately handle distribution shifts in the context distribution and shifts in the reward generating process. |
Tong Mu; Yash Chandak; Tatsunori Hashimoto; Emma Brunskill; |
| 376 | Scalable Interpretability Via Polynomials Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Second degree polynomials can be used as drop-in replacements for DNNs on most tabular and processed image datasets for interpretability with no loss in performance. |
Abhimanyu Dubey; Filip Radenovic; Dhruv Mahajan; |
| 377 | Diffusion Models As Plug-and-Play Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We consider the problem of inferring high-dimensional data $x$ in a model that consists of a prior $p(x)$ and an auxiliary constraint $c(x,y)$. |
Alexandros Graikos; Nikolay Malkin; Nebojsa Jojic; Dimitris Samaras; |
| 378 | Non-Gaussian Tensor Programs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We have extended the Tensor Programs framework to non-Gaussian weight distributions and recovered all existing applications of its main theorem |
Eugene Golikov; Greg Yang; |
| 379 | When to Update Your Model: Constrained Model-based Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We analyze the optimization monotonicity for MBRL algorithms under a novel and general scheme, upon which we develop an algorithm CMLO equipped with an event-triggered mechanism to learn the model from a dynamically-varying number of explorations. |
Tianying Ji; Yu Luo; Fuchun Sun; Mingxuan Jing; Fengxiang He; Wenbing Huang; |
| 380 | Learning to Navigate Wikipedia with Graph Diffusion Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an efficient technique for learning to navigate web knowledge sources like Wikipedia by pretraining on random walks. This navigating agent can be used for precise evidence gathering on downstream tasks like QA and fact verification. |
Manzil Zaheer; Kenneth Marino; Will Grathwohl; John Schultz; Wenling Shang; Sheila Babayan; Arun Ahuja; Ishita Dasgupta; Christine Kaeser-Chen; Rob Fergus; |
| 381 | ZIN: When and How to Learn Invariance By Environment Inference? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a framework to provably learn invariant feature without environment partition. |
Yong Lin; Shengyu Zhu; Lu Tan; Peng Cui; |
| 382 | Information-Theoretic Generative Model Compression with Variational Energy-based Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks. |
Minsoo Kang; Hyewon Yoo; Eunhee Kang; Sehwan Ki; Hyong Euk Lee; Bohyung Han; |
| 383 | Understanding Programmatic Weak Supervision Via Source-aware Influence Function Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a general framework for understanding the behavior of model rendered by Programmatic Weak Supervision (PWS). |
Jieyu Zhang; Haonan Wang; Cheng-Yu Hsieh; Alexander Ratner; |
| 384 | Learning-based Manipulation Planning in Dynamic Environments Using GNNs and Temporal Encoding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a GNN-based neural architecture that involves temporal encoding, and use imitation learning with data aggregation procedures for learning both the embedding and edge prioritization policies. |
Ruipeng Zhang; Chenning Yu; Jingkai Chen; Chuchu Fan; Sicun Gao; |
| 385 | Learning Modular Simulations for Homogeneous Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a modular approach to model the dynamics of homogeneous networks, where the nodes are modeled using a ‘message passing neural ODE’ algorithm, an extension over neural ODE that enables node-node communication. |
Jayesh Gupta; Sai Vemprala; Ashish Kapoor; |
| 386 | UMIX: Improving Importance Weighting for Subpopulation Shift Via Uncertainty-Aware Mixup Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a simple and practical approach called uncertainty-aware mixup (UMIX) to improve previous IW methods by re-weighting the mixed samples. We also provide insightful theoretical analysis to explain why it works. |
Zongbo Han; Zhipeng Liang; Fan Yang; Liu Liu; Lanqing Li; Yatao Bian; Peilin Zhao; Bingzhe Wu; Changqing Zhang; Jianhua Yao; |
| 387 | Where2comm: Communication-Efficient Collaborative Perception Via Spatial Confidence Maps Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. |
Yue Hu; Shaoheng Fang; Zixing Lei; Yiqi Zhong; Siheng Chen; |
| 388 | Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: we propose a Natural Color Fool (NCF), which fully exploits color distributions of semantic classes in an image to craft human-imperceptible, flexible, and highly transferable adversarial examples. |
Shengming Yuan; Qilong Zhang; Lianli Gao; Yaya Cheng; Jingkuan Song; |
| 389 | AutoST: Towards The Universal Modeling of Spatio-temporal Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the manually-designed heterogeneous models can hardly meet the spatio-temporal dependency capturing priority for various tasks. To address this, we proposed a universal modeling framework with three distinctive characteristics: (i) Attention-based network backbone, including S2T Layer (spatial first), T2S Layer (temporal first), and STS Layer (spatio-temporal synchronous). |
Jianxin Li; Shuai Zhang; Hui Xiong; Haoyi Zhou; |
| 390 | On Solving Class Incremental Learning in Continual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper performs a theoretical study on how to solve the class increment learning problem (CIL) and proposes two strong CIL algorithms. |
Gyuhak Kim; Changnan Xiao; Tatsuya Konishi; Zixuan Ke; Bing Liu; |
| 391 | On The Effectiveness of Persistent Homology Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The goal of this work is to identify some types of problems where PH performs well or even better than other state-of-the-art methods in data analysis. |
Renata Turkes; Guido Montufar; Nina Otter; |
| 392 | Learning Debiased Classifier with Biased Committee Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a new method for training debiased classifier, learning debiased classifier with biased committee (LWBC). |
Nayeong Kim; SEHYUN HWANG; Sungsoo Ahn; Jaesik Park; Suha Kwak; |
| 393 | 3DB: A Framework for Debugging Computer Vision Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation. |
Guillaume Leclerc; Hadi Salman; Andrew Ilyas; Sai Vemprala; Logan Engstrom; Vibhav Vineet; Kai Xiao; Pengchuan Zhang; Shibani Santurkar; Greg Yang; Ashish Kapoor; Aleksander Madry; |
| 394 | ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our primary contribution is framing the VS process as a new topology-based graph ranking problem by partitioning a compound into chemical substructures informed by the periodic properties of its atoms and extracting their persistent homology features at multiple resolution levels. |
Andaç Demir; Baris Coskunuzer; Yulia Gel; Ignacio Segovia-Dominguez; Yuzhou Chen; Bulent Kiziltan; |
| 395 | Trading Off Utility, Informativeness, and Complexity in Emergent Communication Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Training agents to communicate according to a tradeoff between utility, communicative accuracy, and complexity allows us to generate varied emergent communication, much like differing human languages. |
Mycal Tucker; Roger Levy; Julie Shah; Noga Zaslavsky; |
| 396 | Grounded Video Situation Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a new task Grounded Video Situation Recognition(GVSR). In addition to predicting the verbs, and semantic roles in the form of captions, we also ground them in the spatio-temporal domain in weakly-supervised setup in an end-to-end fashion. |
Zeeshan Khan; C.V. Jawahar; Makarand Tapaswi; |
| 397 | HierSpeech: Bridging The Gap Between Text and Speech By Hierarchical Variational Inference Using Self-supervised Representations for Speech Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents HierSpeech, a high-quality end-to-end text-to-speech (TTS) system based on a hierarchical conditional variational autoencoder (VAE) utilizing self-supervised speech representations. |
Sang-Hoon Lee; Seung-Bin Kim; Ji-Hyun Lee; Eunwoo Song; Min-Jae Hwang; Seong-Whan Lee; |
| 398 | Structural Kernel Search Via Bayesian Optimization and Symbolical Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a new method for kernel selection for Gaussian processes, where the distance between two GPs is measured using their associated symbolic description of the statistical hypothesis. |
Matthias Bitzer; Mona Meister; Christoph Zimmer; |
| 399 | Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We lay out the theory and practice of devising optimal transportation routes with subadditive edge costs as a generalization of optimal transport, encouraging solutions with branched structure. |
Peter Lippmann; Enrique Fita Sanmartín; Fred Hamprecht; |
| 400 | Understanding Robust Learning Through The Lens of Representation Similarities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we aim to understand how the properties of representations learned by robust training differ from those obtained from standard, non-robust training. |
Christian Cianfarani; Arjun Nitin Bhagoji; Vikash Sehwag; Ben Zhao; Prateek Mittal; Heather Zheng; |
| 401 | A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While sample complexities in MDPs with linear optimal value functions can be exponentially large, we give a new method which shows that a surprisingly-little amount of expert advice permits sample efficiency. |
Philip Amortila; Nan Jiang; Dean Foster; Dhruv Madeka; |
| 402 | Delving Into Sequential Patches for Deepfake Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on low-level temporal inconsistency understanding, we identify deepfake videos in a more robust and generalizable way with model designs in a Transfomer style. |
Jiazhi Guan; Hang Zhou; Zhibin Hong; Errui Ding; Jingdong Wang; Chengbin Quan; Youjian Zhao; |
| 403 | Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This ambiguity suggests that a predictive model should have similar probabilistic characteristics to match the data it models. Therefore, we propose a hierarchical latent distribution to enhance one of the most successful deep learning models, the Transformer, to accommodate these sorts of ambiguities and data distributions. |
Jörg Franke; Frederic Runge; Frank Hutter; |
| 404 | Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We explore how to design the untargeted backdoor watermark and how to use it for harmless and stealthy dataset copyright protection. |
Yiming Li; Yang Bai; Yong Jiang; Yong Yang; Shu-Tao Xia; Bo Li; |
| 405 | Synergy-of-Experts: Collaborate to Improve Adversarial Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper further improves the ensemble’ adversarial robustness through a collaboration scheme. |
Sen Cui; Jingfeng ZHANG; Jian Liang; Bo Han; Masashi Sugiyama; Changshui Zhang; |
| 406 | HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a hybrid sign and distance field for modeling arbitrary shapes with both open and closed surfaces. |
Li Wang; jie Yang; Weikai Chen; Xiaoxu Meng; Bo Yang; Jintao Li; Lin Gao; |
| 407 | Semi-Supervised Semantic Segmentation Via Gentle Teaching Assistant Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a gentle teaching assistant for semi-supervised semantic segmentation, which assists representation learning through our carefully designed representation knowledge transmission. |
Ying Jin; Jiaqi Wang; Dahua Lin; |
| 408 | A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present several structure-exploiting lower and upper bounding methods. |
Kaixun Hua; Jiayang Ren; Yankai Cao; |
| 409 | HSurf-Net: Normal Estimation for 3D Point Clouds By Learning Hyper Surfaces Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. |
Qing Li; Yu-Shen Liu; Jin-San Cheng; Cheng Wang; Yi Fang; Zhizhong Han; |
| 410 | Decoupled Self-supervised Learning for Non-Homophilous Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of conducting self-supervised learning for node representation learning on non-homophilous graphs. |
Teng Xiao; Zhengyu Chen; Zhimeng Guo; Zeyang Zhuang; Suhang Wang; |
| 411 | DataMUX: Data Multiplexing for Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present data multiplexing (DataMUX) — a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation and dramatically improves inference throughput |
Vishvak Murahari; Carlos Jimenez; Runzhe Yang; Karthik Narasimhan; |
| 412 | Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider the combinatorial bandits problem with semi-bandit feedback under finite sampling budget constraints, in which the learner can carry out its action only for a limited number of times specified by an overall budget. |
Jasmin Brandt; Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier; |
| 413 | Saliency-Aware Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: They treat all data elements as being equally important and therefore lead to suboptimal performance. To address this problem, we propose an end-to-end framework which dynamically detects saliency of input data, reweights data using saliency maps, and searches architectures on saliency-reweighted data. |
Ramtin Hosseini; Pengtao Xie; |
| 414 | ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel multi-armed bandit algorithm for the “leveling" task where the aim is to keep the outcomes close to a target level rather than maximize them, which is a prevalent problem in medicine. |
Ilker Demirel; Ahmet Alparslan Celik; Cem Tekin; |
| 415 | Neuron with Steady Response Leads to Better Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Accordingly, we propose a new regularization method called Neuron Steadiness Regularization (NSR) to reduce neuron intra-class response variance. |
Qiang Fu; Lun Du; Haitao Mao; Xu Chen; Wei Fang; Shi Han; Dongmei Zhang; |
| 416 | Learning Active Camera for Multi-Object Navigation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unlike existing agents that always look forward, we propose an active-camera agent that coordinates the camera moving action and navigation action for efficiently perceiving the environment to solve the multi-object navigation task. |
Peihao Chen; Dongyu Ji; Kunyang Lin; Weiwen Hu; Wenbing Huang; Thomas Li; Mingkui Tan; Chuang Gan; |
| 417 | SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a Semantic-Prototype Variational Transformer (SPoVT) for dense point cloud semantic completion. |
Sheng Yu Huang; Hao-Yu Hsu; Frank Wang; |
| 418 | Debiased Self-Training for Semi-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We tackle the bias issue in SSL by (1) decoupling the generation and utilization of pseudo labels; (2) estimating the worst case of pseudo labeling and optimizing the representation to avoid the worst case. |
Baixu Chen; Junguang Jiang; Ximei Wang; Pengfei Wan; Jianmin Wang; Mingsheng Long; |
| 419 | Disentangling The Predictive Variance of Deep Ensembles Through The Neural Tangent Kernel Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By studying deep ensembles in the linear training regime, we can describe their predictive variance through the Neural Tangent Kernel. |
Seijin Kobayashi; Pau Vilimelis Aceituno; Johannes von Oswald; |
| 420 | Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an Uncertainty-Guided Pseudo Label Generator and introduce an adversarial learning strategy to improve the quality of pseudo-labels, finally solving SS-VSOD by using the progressively-enhanced pseudo labels. |
chenyang lu; Yongri Piao; Miao Zhang; Huchuan Lu; |
| 421 | Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel communication-efficient learning method called stripe-wise group quantization (SGQ), which significantly reduces feature size and communication traffic, while not degrading model accuracy for edge-cloud systems. |
Qihua ZHOU; Song Guo; YI LIU; Jie Zhang; Jiewei Zhang; Tao GUO; Zhenda XU; Zhihao Qu; |
| 422 | RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel contrastive learning method that uses Rényi divergence to manage harder data augmentations. |
Kyungmin Lee; Jinwoo Shin; |
| 423 | Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This presents a challenge for all theorem provers, especially the ones based on language models, due to their relative inability to reason over huge volumes of premises in text form. This paper introduces Thor, a framework integrating language models and automated theorem provers to overcome this difficulty. |
Albert Qiaochu Jiang; Wenda Li; Szymon Tworkowski; Konrad Czechowski; Tomasz Odrzygóźdź; Piotr Miłoś; Yuhuai Wu; Mateja Jamnik; |
| 424 | Autoformalization with Large Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Large language models can be used to do autoformalization, allowing us to achieve in a new SOTA on miniF2F benchmark. |
Yuhuai Wu; Albert Qiaochu Jiang; Wenda Li; Markus N Rabe; Charles Staats; Mateja Jamnik; Christian Szegedy; |
| 425 | Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a probabilistic imputation method using an extended Gaussian copula model that supports both single and multiple imputation. |
Yuxuan Zhao; Alex Townsend; Madeleine Udell; |
| 426 | TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The main reason is the image region occupied by these parts is very small compared to the body. To solve this problem, we propose TotalSelfScan, which reconstructs the full-body human from several monocular self-rotation videos that focus on the face, hands, and body, respectively. |
Junting Dong; Qi Fang; Yudong Guo; Sida Peng; Qing Shuai; Hujun Bao; Xiaowei Zhou; |
| 427 | Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce the multi-action-branch reward estimation followed by policy-weighted reward aggregation for stabilized training in multi-agent reinforcement learning with reward uncertainty. |
Jifeng Hu; Yanchao Sun; Hechang Chen; Sili Huang; haiyin piao; Yi Chang; Lichao Sun; |
| 428 | Unsupervised Cross-Domain Imitation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a scalable framework that enables cross-domain imitation learning without access to additional demonstrations or further domain knowledge. |
Tim Franzmeyer; Philip Torr; João Henriques; |
| 429 | Function Classes for Identifiable Nonlinear Independent Component Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We prove identifiability results for nonlinear Independent Component Analysis in constrained function classes |
Simon Buchholz; Michel Besserve; Bernhard Schölkopf; |
| 430 | Tree Ensemble Kernels for Bayesian Optimization with Known Constraints Over Mixed-feature Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We use tree kernel Gaussian processes for Bayesian optimization to simultaneously incorporate: a reliable uncertainty metric in mixed features and known constraints. |
Alexander Thebelt; Calvin Tsay; Robert Lee; Nathan Sudermann-Merx; David Walz; Behrang Shafei; Ruth Misener; |
| 431 | OnePose++: Keypoint-Free One-Shot Object Pose Estimation Without CAD Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an keypoint-free one-shot object pose estimation method that handles low-textured objects without knowing CAD models. |
Xingyi He; Jiaming Sun; Yuang Wang; Di Huang; Hujun Bao; Xiaowei Zhou; |
| 432 | Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper studies a collaborative linear bandit setting in the presence of adversaries, proposes several novel robust algorithms, and provides the first set of tight regret bounds for this problem. |
Aritra Mitra; Arman Adibi; George J. Pappas; Hamed Hassani; |
| 433 | Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: In this paper, we study the episodic reinforcement learning (RL) problem modeled by finite-horizon Markov Decision Processes (MDPs) with constraint on the number of batches. The … |
Zihan Zhang; Yuhang Jiang; Yuan Zhou; Xiangyang Ji; |
| 434 | Contextual Bandits with Knapsacks for A Conversion Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a model of contextual bandits with knapsacks where rewards and costs are coupled through binary variables measuring customer conversions |
Zhen LI; Gilles Stoltz; |
| 435 | Factuality Enhanced Language Models for Open-Ended Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a factual-nucleus sampling algorithm that dynamically adapts the randomness to improve the factuality of generation while maintaining quality. |
Nayeon Lee; Wei Ping; Peng Xu; Mostofa Patwary; Mohammad Shoeybi; Bryan Catanzaro; |
| 436 | Learning (Very) Simple Generative Models Is Hard Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We prove the first computational hardness result for learning pushforwards of Gaussians under one hidden layer ReLU networks of logarithmic size. |
Sitan Chen; Jerry Li; Yuanzhi Li; |
| 437 | Decomposed Knowledge Distillation for Class-incremental Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a simple yet effective framework that achieves a good trade-off between plasticity and rigidity for class-incremental semantic segmentation. |
Donghyeon Baek; Youngmin Oh; Sanghoon Lee; Junghyup Lee; Bumsub Ham; |
| 438 | Escaping from The Barren Plateau Via Gaussian Initializations in Deep Variational Quantum Circuits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a Gaussian initialization strategy addressing the vanishing gradient problem in variational quantum circuits with theoretical guarantees. |
Kaining Zhang; Liu Liu; Min-Hsiu Hsieh; Dacheng Tao; |
| 439 | Predicting from Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study conditions under which the causal effect of performative predictions can be identified from observational data |
Frances Ding; Yixin Wang; Celestine Mendler-Dünner; |
| 440 | Learning Generalizable Models for Vehicle Routing Problems Via Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a generic and efficient Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme to tackle the cross-distribution generalization issue for learning-to-solve routing problems. |
Jieyi Bi; Yining Ma; Jiahai Wang; Zhiguang Cao; Jinbiao Chen; Yuan Sun; Yeow Meng Chee; |
| 441 | A Regret-Variance Trade-Off in Online Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We state a regret-variance trade-off in online learning and provide multiple applications. |
Dirk van der Hoeven; Nikita Zhivotovskiy; Nicolò Cesa-Bianchi; |
| 442 | Learning on The Edge: Online Learning with Stochastic Feedback Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a generalization of adversarial online learning with feedback graph and we prove matching upper and lower bounds on the regret |
Emmanuel Esposito; Federico Fusco; Dirk van der Hoeven; Nicolò Cesa-Bianchi; |
| 443 | NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. |
Yi-Ling Qiao; Alexander Gao; Ming Lin; |
| 444 | Lethal Dose Conjecture on Data Poisoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose Lethal Dose Conjecture, which characterizes the largest amount of poisoned samples any defense can tolerate for a given task, and showcase its implications, including better/easy ways to improve robustness against data poisoning. |
Wenxiao Wang; Alexander Levine; Soheil Feizi; |
| 445 | Mask Matching Transformer for Few-Shot Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. |
siyu jiao; Gengwei Zhang; Shant Navasardyan; Ling Chen; Yao Zhao; Yunchao Wei; Honghui Shi; |
| 446 | AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose AUTOMATA, a gradient-based subset selection framework for hyper-parameter tuning. |
Krishnateja Killamsetty; Guttu Sai Abhishek; Aakriti Lnu; Alexandre Evfimievski; Lucian Popa; Ganesh Ramakrishnan; Rishabh Iyer; |
| 447 | Orient: Submodular Mutual Information Measures for Data Subset Selection Under Distribution Shift Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to improve the efficiency of existing supervised domain adaptation (SDA) methods by using a subset of source data that is similar to target data for faster model training. |
Athresh Karanam; Krishnateja Killamsetty; Harsha Kokel; Rishabh Iyer; |
| 448 | Empirical Gateaux Derivatives for Causal Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study a constructive procedure that approximates Gateaux derivatives for statistical functionals by finite-differencing, with attention to causal inference functionals. |
Michael Jordan; Yixin Wang; Angela Zhou; |
| 449 | Active Model Adaptation Under Changed Distributions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work mainly discusses how to make a known model adapt to a variety of changed distributions at a relatively small labeling cost. |
Jie-Jing Shao; Lan-Zhe Guo; Xiao-wen Yang; Yu-Feng Li; |
| 450 | Enhanced Latent Space Blind Model for Real Image Denoising Via Alternative Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising. |
Chao Ren; Yizhong Pan; Jie Huang; |
| 451 | Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. |
Xinhang Liu; Jiaben Chen; Huai Yu; Yu-Wing Tai; Chi-Keung Tang; |
| 452 | Trap and Replace: Defending Backdoor Attacks By Trapping Them Into An Easy-to-Replace Subnetwork Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a brand-new backdoor defense strategy, which makes it much easier to remove the harmful influence of backdoor samples from the model. |
Haotao Wang; Junyuan Hong; Aston Zhang; Jiayu Zhou; Zhangyang Wang; |
| 453 | Preservation of The Global Knowledge By Not-True Distillation in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper suggests the forgetting global knowledge in federated learning, and proposes distillation-based algorithms to relieve it. |
Gihun Lee; Minchan Jeong; Yongjin Shin; Sangmin Bae; Se-Young Yun; |
| 454 | Adversarial Training for High-stakes Reliability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We used a safe language generation task (“avoid injuries”) as a testbed for achieving high reliability through adversarial training. |
Daniel Ziegler; Seraphina Nix; Lawrence Chan; Tim Bauman; Peter Schmidt-Nielsen; Tao Lin; Adam Scherlis; Noa Nabeshima; Benjamin Weinstein-Raun; Daniel de Haas; Buck Shlegeris; Nate Thomas; |
| 455 | Learning Substructure Invariance for Out-of-Distribution Molecular Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We aim to solve the out-of-distribution problem on molecule representation learning tasks from a substructure invariance perspective. |
Nianzu Yang; Kaipeng Zeng; Qitian Wu; Xiaosong Jia; Junchi Yan; |
| 456 | Confident Adaptive Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce Confident Adaptive Language Modeling (CALM), a framework for dynamically allocating different amounts of compute per input and generation timestep. |
Tal Schuster; Adam Fisch; Jai Gupta; Mostafa Dehghani; Dara Bahri; Vinh Tran; Yi Tay; Donald Metzler; |
| 457 | On Sample Optimality in Personalized Collaborative and Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the sample complexity of collaboratively minimizing functions held by N different agents: we prove matching lower and upper complexity bounds. |
Mathieu Even; Laurent Massoulié; Kevin Scaman; |
| 458 | RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present Robust Adversarial Model-Based Offline RL (RAMBO), a novel approach to model-based offline RL. |
Marc Rigter; Bruno Lacerda; Nick Hawes; |
| 459 | Quo Vadis: Is Trajectory Forecasting The Key Towards Long-Term Multi-Object Tracking? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Intuitively, the longer the occlusion gap, the larger the search space for possible associations. In this paper, we show that even a small yet diverse set of trajectory predictions for moving agents will significantly reduce this search space and thus improve long-term tracking robustness. |
Patrick Dendorfer; Vladimir Yugay; Aljosa Osep; Laura Leal-Taixé; |
| 460 | Efficient Identification of Informative Features in Simulation-based Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Here, we provide a more efficient approach based on the SBI method neural likelihood estimation (NLE): We show that one can marginalize the trained surrogate likelihood post-hoc before inferring the posterior to assess the contribution of a feature.. |
Jonas Beck; Michael Deistler; Yves Bernaerts; Jakob H Macke; Philipp Berens; |
| 461 | Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To allow asynchronous learning and decision-making, we formulate a set of asynchronous multi-agent actor-critic methods that allow agents to directly optimize asynchronous policies in three standard training paradigms: decentralized learning, centralized learning, and centralized training for decentralized execution. |
Yuchen Xiao; Weihao Tan; Christopher Amato; |
| 462 | A Scalable Tester for Samplers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Constrained samplers generate samples from hard distributions. We present a tool that can test whether your sampler does actually generate samples from the right distribution. |
Yash Pote; Kuldeep S Meel; |
| 463 | Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel framework to finetune the connections of speech SSL models, instead of model weights, to empower efficient multilingual and multitask speech processing. |
Yonggan Fu; Yang Zhang; Kaizhi Qian; Zhifan Ye; Zhongzhi Yu; Cheng-I Jeff Lai; Yingyan Lin; |
| 464 | Unsupervised Learning of Group Invariant and Equivariant Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an unsupervised learning framework to extract separated group invariant and equivariant representations. |
Robin Winter; Marco Bertolini; Tuan Le; Frank Noe; Djork-Arné Clevert; |
| 465 | [Re] Replication Study of "Fairness and Bias in Online Selection" Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Scope of Reproducibility This report aims to reproduce the results in the paper ‘Fairness and Bias in Online Selection’. |
Diego van der Mast; Soufiane Ben Haddou; Jacky Chu; Jaap Stefels; |
| 466 | Focal Modulation Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose focal modulation network (FocalNet in short), where self-attention (SA) is completely replaced by a focal modulation module that is more effective and efficient for modeling token interactions. |
Jianwei Yang; Chunyuan Li; Xiyang Dai; Jianfeng Gao; |
| 467 | Reinforcement Learning with Neural Radiance Fields Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We learn state representations of scenes using supervision from neural radiance fields, and show that using these in downstream reinforcement learning tasks improves sample efficiency. |
Danny Driess; Ingmar Schubert; Pete Florence; Yunzhu Li; Marc Toussaint; |
| 468 | MaskPlace: Fast Chip Placement Via Reinforced Visual Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes an RL-based chip placement method MaskPlace based on rich visual representation. |
Yao Lai; Yao Mu; Ping Luo; |
| 469 | When Is The Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper provides an affirmative answer to this problem for the case of either Lipschitz or smooth convex functions with normal priors. By viewing Langevin algorithm as composite optimization, we develop a new analysis technique that leads to dimension independent convergence rates for such problems. |
Yoav S Freund; Yi-An Ma; Tong Zhang; |
| 470 | SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce SAVi++, an object-centric video model which is trained to predict depth signals from a slot-based video representation. SAVi++ is able to learn emergent object segmentation and tracking from videos in the real-world Waymo Open dataset. |
Gamaleldin Elsayed; Aravindh Mahendran; Sjoerd van Steenkiste; Klaus Greff; Michael Mozer; Thomas Kipf; |
| 471 | A Mixture Of Surprises for Unsupervised Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Hence, choosing between the two objectives is a dilemma. We propose a novel yet simple mixture of policies to address this concern, allowing us to optimize an objective that simultaneously maximizes and minimizes the surprise. |
Andrew Zhao; Matthieu Lin; Yangguang Li; Yong-jin Liu; Gao Huang; |
| 472 | Leveraging The Hints: Adaptive Bidding in Repeated First-Price Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Following a series of recent works in this area, we consider a differentiated setup: we do not make any assumption about other bidders’ maximum bid (i.e. it can be adversarial over time), and instead assume that we have access to a hint that serves as a prediction of other bidders’ maximum bid, where the prediction is learned through some blackbox machine learning model. |
Wei Zhang; Yanjun Han; Zhengyuan Zhou; Aaron Flores; Tsachy Weissman; |
| 473 | Amortised Inference in Structured Generative Models with Explaining Away Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose structured amortised inference to account for the posterior latent correlations induced by the "explaining away" effect. |
Changmin Yu; Hugo Soulat; Neil Burgess; Maneesh Sahani; |
| 474 | AD-DROP: Attribution Driven Dropout for Robust Language Model Finetuning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the impact of dropout on self-attention and propose a novel dropout regularizer, AD-DROP, driven by self-attention attribution to reduce overfitting when fine-tuning pre-trained language models. |
Tao Yang; JInghao Deng; Xiaojun Quan; Qifan Wang; Shaoliang Nie; |
| 475 | Robust and Scalable Manifold Learning Via Landmark Diffusion for Long-term Medical Signal Processing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by analyzing long-term physiological time series, we design a robust and scalable spectral embedding algorithm that we refer to as RObust and Scalable Embedding via LANdmark Diffusion (Roseland). |
Chao Shen; Yu-Ting Lin; Hau-Tieng Wu; |
| 476 | Toward Robust Spiking Neural Network Against Adversarial Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The first work that applies certification-based techniques to spiking neural networks. |
LING LIANG; Kaidi Xu; Xing Hu; Lei Deng; Yuan Xie; |
| 477 | Most Activation Functions Can Win The Lottery Without Excessive Depth Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We generalize lottery ticket existence proofs to almost arbitrary activation functions and show that a source network can have almost the same depth as a target network. |
Rebekka Burkholz; |
| 478 | LGDN: Language-Guided Denoising Network for Video-Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an efficient and effective model for video-language modeling with salient frame proposal mechanism. |
Haoyu Lu; Mingyu Ding; Nanyi Fei; Yuqi Huo; Zhiwu Lu; |
| 479 | Online Nonnegative CP-dictionary Learning for Markovian Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce a novel algorithm that learns a CANDECOMP/PARAFAC (CP) basis from a given stream of tensor-valued data under general constraints, including nonnegativity constraints that induce interpretability of the learned CP basis. |
Hanbaek Lyu; Christopher Strohmeier; Deanna Needell; |
| 480 | Exploring Example Influence in Continual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We explore the example influence in Continual Learning, and give the usage of example influence. |
Qing Sun; Fan Lyu; Fanhua Shang; Wei Feng; Liang Wan; |
| 481 | Navigating Memory Construction By Global Pseudo-Task Simulation for Continual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We have proposed a novel method Global Pseudo-task Simulation (GPS) to solve the dynamic memory construction problem in the online continual learning setting. |
Yejia Liu; Wang Zhu; Shaolei Ren; |
| 482 | SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents a novel convolutional neural network for time series forecasting, achieving significant accuracy improvements. |
Minhao LIU; Ailing Zeng; Muxi Chen; Zhijian Xu; Qiuxia LAI; Lingna Ma; Qiang Xu; |
| 483 | Multi-Agent Multi-Armed Bandits with Limited Communication Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present Limited Communication Collaboration – Upper Confidence Bound (LCC-UCB), a doubling-epoch based algorithm where each agent communicates only after the end of the epoch and shares the index of the best arm it knows. |
Mridul Agarwal; Vaneet Aggarwal; Kamyar Azizzadenesheli; |
| 484 | Concentration of Data Encoding in Parameterized Quantum Circuits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work shows the concentration of data encoding in parameterized quantum circuits and its severe limitations on downstream tasks. |
Guangxi Li; Ruilin Ye; Xuanqiang Zhao; Xin Wang; |
| 485 | Robust Model Selection and Nearly-Proper Learning for GMMs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We give efficient algorithms for robust model selection and nearly-proper learning of Gaussian mixture models. |
Allen Liu; Jerry Li; Ankur Moitra; |
| 486 | Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and Its Application to Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Especially, its variance reduced versions have nowadays gained particular attention. In this paper, we study two variants of this kind, namely, the Stochastic Variance Reduced Gradient Langevin Dynamics and the Stochastic Recursive Gradient Langevin Dynamics. |
Yuri Kinoshita; Taiji Suzuki; |
| 487 | Imbalance Trouble: Revisiting Neural-Collapse Geometry Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we thus ask whether it can be made invariant to class imbalances. Towards this end, we adopt the unconstrained feature model (UFM), a recent theoretical model for studying neural collapse, and introduce $\text{\emph{Simplex-Encoded-Labels Interpolation}}$ (SELI) as an invariant characterization of the neural collapse phenomenon. |
Christos Thrampoulidis; Ganesh Ramachandra Kini; Vala Vakilian; Tina Behnia; |
| 488 | Understanding and Improving Robustness of Vision Transformers Through Patch-based Negative Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the robustness of vision transformers (ViTs) through the lens of their special patch-based architectural structure, i.e., they process an image as a sequence of image patches. |
Yao Qin; Chiyuan Zhang; Ting Chen; Balaji Lakshminarayanan; Alex Beutel; Xuezhi Wang; |
| 489 | Animatable 3D-Aware Face Image Generation for Realistic Video Avatars Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an animatable 3D-aware face image generation method. |
Yue Wu; Yu Deng; Jiaolong Yang; Fangyun Wei; Qifeng Chen; Xin Tong; |
| 490 | Normalizing Flows for Knockoff-free Controlled Feature Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Using a normalizing flow to fit an arbitrary feature distribution, our method identifies relevant features while controlling false discoveries. |
Derek Hansen; Brian Manzo; Jeffrey Regier; |
| 491 | Learning to Break The Loop: Analyzing and Mitigating Repetitions for Neural Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We analyze the consetutive sentence repetitions in language models and propose a simple and effective method to mitigate it. |
Jin Xu; Xiaojiang Liu; Jianhao Yan; Deng Cai; Huayang Li; Jian Li; |
| 492 | RCNNs Learn Succinct Learning Algorithms in Polynomial Time Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We describe a natural architecture with recurrent and convolutional weight sharing which, when trained using SGD with random initialization and restarts, can perform as well as all constant sized TMs. |
Surbhi Goel; Cyril Zhang; Sham Kakade; Adam Kalai; |
| 493 | Hidden Progress in Deep Learning: SGD Learns Parities Near The Computational Limit Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While there are some accounts of how these resources modulate statistical capacity, far less is known about their effect on the computational problem of model training. This work conducts such an exploration through the lens of learning $k$-sparse parities of $n$ bits, a canonical family of problems which pose theoretical computational barriers. |
Boaz Barak; Benjamin Edelman; Surbhi Goel; Sham Kakade; Eran Malach; Cyril Zhang; |
| 494 | Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper understands and improves residual networks from a social psychology perspective of loafing |
Peng Ye; Shengji Tang; Baopu Li; Tao Chen; Wanli Ouyang; |
| 495 | Diffusion Visual Counterfactual Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Current approaches for the generation of VCEs are restricted to adversarially robust models and often contain non-realistic artefacts or are restricted to image classification problems with few classes. In this paper we overcome this by generating Diffusion Visual Counterfactual Explanations (DVCEs) for arbitrary ImageNet classifiers via a diffusion process. |
Maximilian Augustin; Valentyn Boreiko; Francesco Croce; Matthias Hein; |
| 496 | Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For optimization problems with a sparsity constraint, we propose acceleration methods with provably faster convergence rates and significantly faster empirical speed than the state of the art. |
Jan Harold Alcantara; Ching-pei Lee; |
| 497 | TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on. |
Pengfei Li; Beiwen Tian; Yongliang Shi; Xiaoxue Chen; Hao Zhao; Guyue Zhou; Ya-Qin Zhang; |
| 498 | DENSE: Data-Free One-Shot Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Despite the low communication cost, existing one-shot FL methods are mostly impractical or face inherent limitations, \eg a public dataset is required, %poor performance of the global model, clients’ models are homogeneous, and additional data/model information need to be uploaded. To overcome these issues, we propose a novel two-stage \textbf{D}ata-fre\textbf{E} o\textbf{N}e-\textbf{S}hot federated l\textbf{E}arning (DENSE) framework, which trains the global model by a data generation stage and a model distillation stage. |
Jie Zhang; Chen Chen; Bo Li; Lingjuan Lyu; Shuang Wu; Shouhong Ding; Chunhua Shen; Chao Wu; |
| 499 | A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work develops a stochastic linearized augmented Lagrangian method (SLAM) for solving general nonconvex bilevel optimization problems over a graph, where both upper and lower optimization variables are able to achieve a consensus. |
Songtao Lu; Siliang Zeng; Xiaodong Cui; Mark Squillante; Lior Horesh; Brian Kingsbury; Jia Liu; Mingyi Hong; |
| 500 | Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide fine-grained analysis of stability and generalization for modern meta learning algorithms. |
Jiechao Guan; Yong Liu; Zhiwu Lu; |
| 501 | Quasi-Newton Methods for Saddle Point Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose random Broyden family updates, which have explicit local superlinear convergence rate of ${\mathcal O}\big(\big(1-1/(n\kappa^2)\big)^{k(k-1)/2}\big)$, where $n$ is the dimension of the problem, $\kappa$ is the condition number and $k$ is the number of iterations. |
Chengchang Liu; Luo Luo; |
| 502 | Training Language Models to Follow Instructions with Human Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We fine-tune GPT-3 using data collected from human labelers. The resulting model, called InstructGPT, outperforms GPT-3 on a range of NLP tasks. |
Long Ouyang; Jeffrey Wu; Xu Jiang; Diogo Almeida; Carroll Wainwright; Pamela Mishkin; Chong Zhang; Sandhini Agarwal; Katarina Slama; Alex Ray; John Schulman; Jacob Hilton; Fraser Kelton; Luke Miller; Maddie Simens; Amanda Askell; Peter Welinder; Paul Christiano; Jan Leike; Ryan Lowe; |
| 503 | Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in Langevin equation with a deterministic density gradient term. |
Uros Seljak; Richard Grumitt; Biwei Dai; |
| 504 | Batch Size-invariance for Policy Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show how to make PPO batch size-invariant (changes to the batch size can largely be compensated for by changing other hyperparameters) by decoupling the proximal policy (used for controlling the size of policy updates) from the behavior policy. |
Jacob Hilton; Karl Cobbe; John Schulman; |
| 505 | ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by learning of linear problems, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data). |
HUIPING ZHUANG; Zhenyu Weng; Hongxin Wei; RENCHUNZI XIE; Kar-Ann Toh; Zhiping Lin; |
| 506 | Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show that distributed learning setup has a smaller generalization error than the corresponding centralized setup, for certain cases. |
Milad Sefidgaran; Romain Chor; Abdellatif Zaidi; |
| 507 | Multi-agent Dynamic Algorithm Configuration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose MA-DAC to solve the dynamic configuration of algorithms with multiple types of hyperparameters, where one agent works to handle one type of configuration hyperparameter. |
Ke Xue; Jiacheng Xu; Lei Yuan; Miqing Li; Chao Qian; Zongzhang Zhang; Yang Yu; |
| 508 | Weakly Supervised Knowledge Distillation for Whole Slide Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an end-to-end weakly supervised knowledge distillation framework (WENO) for WSI classification. |
Linhao Qu; xiaoyuan luo; Manning Wang; Zhijian Song; |
| 509 | Semantic Exploration from Language Abstractions and Pretrained Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Novelty-based exploration methods can suffer in high-dimensional state spaces, such as continuous partially-observable 3D environments. We address this challenge by defining novelty using semantically meaningful state abstractions, which can be found in learned representations shaped by natural language. |
Allison Tam; Neil Rabinowitz; Andrew Lampinen; Nicholas Roy; Stephanie Chan; DJ Strouse; Jane Wang; Andrea Banino; Felix Hill; |
| 510 | Curious Exploration Via Structured World Models Yields Zero-Shot Object Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose CEE-US, a method combining the learning of GNNs as structured world models with curiosity-driven, planning-based exploration, that achieves zero-shot downstream task generalization in multi-object manipulation tasks. |
Cansu Sancaktar; Sebastian Blaes; Georg Martius; |
| 511 | Multi-Objective Bayesian Optimization with Pareto Set Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel Pareto set learning (PSL) method to approximate the whole Pareto set for expensive multi-objective optimization problems. |
Xi Lin; Zhiyuan Yang; Xiaoyuan Zhang; Qingfu Zhang; |
| 512 | Robust Binary Models By Pruning Randomly-initialized Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a framework to find robust sub-networks from randomly-initialized binary networks without updating the model parameters. |
Chen Liu; Ziqi Zhao; Sabine Süsstrunk; Mathieu Salzmann; |
| 513 | A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We extend the study on PLM subnetwork to the OOD scenario,investigating whether there exist PLM subnetworks that are both sparse and robust against dataset bias. |
Yuanxin Liu; Fandong Meng; Zheng Lin; Jiangnan Li; Peng Fu; Yanan Cao; Weiping Wang; Jie Zhou; |
| 514 | Hierarchical Normalization for Robust Monocular Depth Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel multi-scale depth normalization method that hierarchically normalizes the depth representations based on spatial information and depth distributions. |
Chi Zhang; Wei Yin; Billzb Wang; Gang Yu; Chunhua Shen; BIN FU; |
| 515 | Integral Probability Metrics PAC-Bayes Bounds Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). |
Ron Amit; Baruch Epstein; Shay Moran; Ron Meir; |
| 516 | AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines By Linking Keypoints Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a self-supervised method that learns the common object structure as a graph that links keypoints to skeletons. |
Xingzhe He; Bastian Wandt; Helge Rhodin; |
| 517 | DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, in practice, GAN-based offline RL methods have not outperformed alternative approaches, perhaps because the generator is trained to both fool the discriminator and maximize return – two objectives that are often at odds with each other. In this paper, we show that the issue of conflicting objectives can be resolved by training two generators: one that maximizes return, with the other capturing the remainder of the data distribution in the offline dataset, such that the mixture of the two is close to the behavior policy. |
Quan Vuong; Aviral Kumar; Sergey Levine; Yevgen Chebotar; |
| 518 | Embrace The Gap: VAEs Perform Independent Mechanism Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The gap between ELBO and log-likelihood helps variational autoencoders with near-deterministic decoders learn useful representations by performing independent mechanism analysis. |
Patrik Reizinger; Luigi Gresele; Jack Brady; Julius von Kügelgen; Dominik Zietlow; Bernhard Schölkopf; Georg Martius; Wieland Brendel; Michel Besserve; |
| 519 | Disentangling Causal Effects from Sets of Interventions in The Presence of Unobserved Confounders Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We formally characterise the conditions under which single-variable causal effects can be learnt from only observational and multi-variable interventional data — providing identification proofs alongside an estimation method we evaluate empirically. |
Olivier Jeunen; Ciarán Gilligan-Lee; Rishabh Mehrotra; Mounia Lalmas; |
| 520 | To Update or Not to Update? Neurons at Equilibrium in Deep Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work we shift our focus from the single parameters to the behavior of the whole neuron, exploiting the concept of neuronal equilibrium (NEq). |
Andrea Bragagnolo; Enzo Tartaglione; Marco Grangetto; |
| 521 | An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To leverage the segment structure of piecewise stable context in real-world applications, in this paper, we propose a \textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented \textbf{D}eep~(SeCBAD)} RL method. |
Xiaoyu Chen; Xiangming Zhu; Yufeng Zheng; Pushi Zhang; Li Zhao; Wenxue Cheng; Peng CHENG; Yongqiang Xiong; Tao Qin; Jianyu Chen; Tie-Yan Liu; |
| 522 | SHAQ: Incorporating Shapley Value Theory Into Multi-Agent Q-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Based on the properties of MSV, we derive \textit{Shapley-Bellman optimality equation} (SBOE) to evaluate the optimal MSV, which corresponds to an optimal joint deterministic policy. |
Jianhong Wang; Yuan Zhang; Yunjie Gu; Tae-Kyun Kim; |
| 523 | BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel cross-modal BMU-MoCo with bidirectional momentum update for continual video-language modeling. |
Yizhao Gao; Nanyi Fei; Haoyu Lu; Zhiwu Lu; Hao Jiang; Yijie Li; Zhao Cao; |
| 524 | Structural Analysis of Branch-and-Cut and The Learnability of Gomory Mixed Integer Cuts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We conduct a novel structural analysis of branch-and-cut that pins down how every step of the algorithm is affected by changes in the parameters defining the cutting planes added to the input integer program. |
Maria-Florina Balcan; Siddharth Prasad; Tuomas Sandholm; Ellen Vitercik; |
| 525 | Joint Entropy Search for Multi-Objective Bayesian Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose the Joint Entropy Search acquisition function for multi-objective Bayesian optimization and showcase its effectiveness on some practical problems. |
Ben Tu; Axel Gandy; Nikolas Kantas; Behrang Shafei; |
| 526 | GAR: Generalized Autoregression for Multi-Fidelity Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite the fast developments of multi-?delity fusion techniques, most existing methods require particular data structures and do not scale well to high-dimensional output. To resolve these issues, we generalize the classic autoregression (AR), which is wildly used due to its simplicity, robustness, accuracy, and tractability, and propose generalized autoregression (GAR) using tensor formulation and latent features. |
Yuxin Wang; Zheng Xing; WEI XING; |
| 527 | Learning The Structure of Large Networked Systems Obeying Conservation Laws Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Motivated by this important problem, we study the estimation of the sparsity structure of the matrix $B^\ast$ from $n$ samples of $Y$ under the assumption that the node injections $X$ follow a Gaussian distribution with a known covariance $\Sigma_X$. We propose a new $\ell_{1}$-regularized maximum likelihood estimator for tackling this problem in the high-dimensional regime where the size of the network may be vastly larger than the number of samples $n$. |
Anirudh Rayas; Rajasekhar Anguluri; Gautam Dasarathy; |
| 528 | CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We develop a model which uses task-agnostic and task-specific demonstrations both explicitly and implicitly to improve efficiency of reinforcement learning |
Kai Yan; Alex Schwing; Yu-Xiong Wang; |
| 529 | The Neural Testbed: Evaluating Joint Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open source benchmark for controlled and principled evaluation of agents that generate such predictions. |
Ian Osband; Zheng Wen; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; MORTEZA IBRAHIMI; Dieterich Lawson; Botao Hao; Brendan O’Donoghue; Benjamin Van Roy; |
| 530 | Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider Follow-the-Perturbed-Leader (FTPL) algorithms for Adversarial Markov Decision Processes (AMDPs) in episodic settings. We also extend them to delayed AMDPs as well as infinite-horizon communicating AMDPs. |
Yan Dai; Haipeng Luo; Liyu Chen; |
| 531 | On The Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the finite time global convergence to a Nash equilibrium for decentralized softmax gradient play algorithms under the Markov potential game setting. |
Runyu Zhang; Jincheng Mei; Bo Dai; Dale Schuurmans; Na Li; |
| 532 | Generative Status Estimation and Information Decoupling for Image Rain Removal Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We construct SEIDNet, a generative network equipped with the pixel-wise Status Estimation and the Information Decoupling for rain removal. |
Di Lin; Xin WANG; Jia Shen; Renjie Zhang; Ruonan Liu; Miaohui Wang; Wuyuan Xie; Qing Guo; Ping Li; |
| 533 | Learning to Compare Nodes in Branch and Bound with Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Branch-and-bound approaches in integer programming require ordering portions of the space to explore next, a problem known as node comparison. We propose a new siamese graph neural network model to tackle this problem, where the nodes are represented as bipartite graphs with attributes. |
Abdel Ghani Labassi; Didier Chetelat; Andrea Lodi; |
| 534 | Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes Under Non-Parametric Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We establish first finite sample error bounds for OPE in confounded POMDPs under non-parametric models. |
Rui Miao; Zhengling Qi; Xiaoke Zhang; |
| 535 | UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a unified model for computer vision, which does not require any task-specific components. |
Alexander Kolesnikov; André Susano Pinto; Lucas Beyer; Xiaohua Zhai; Jeremiah Harmsen; Neil Houlsby; |
| 536 | InterpretDL: Explaining Deep Models in PaddlePaddle Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce InterpretDL, a toolkit of explanation algorithms based on PaddlePaddle, with uniformed programming interfaces and "plug-and-play" designs. |
Xuhong Li; Haoyi Xiong; Xingjian Li; Xuanyu Wu; Zeyu Chen; Dejing Dou; |
| 537 | EfficientViT: Vision Transformers at MobileNet Speed Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. |
Yanyu Li; Geng Yuan; Yang Wen; Ju Hu; Georgios Evangelidis; Sergey Tulyakov; Yanzhi Wang; Jian Ren; |
| 538 | Joint Estimation and Inference for Data Integration Problems Based on Multiple Multi-layered Gaussian Graphical Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span multiple layers, like genomic, proteomic or metabolomic data, and further pertain to multiple sources, like disease subtypes or experimental conditions. In this work, we propose a general statistical framework based on Gaussian graphical models for horizontal (i.e. across conditions or subtypes) and vertical (i.e. across different layers containing data on molecular compartments) integration of information in such datasets. |
Subhabrata Majumdar; George Michailidis; |
| 539 | Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We improve 3D-aware GANs by making the discriminator 3D-aware as well, resulting in far more accurate 3D shapes. |
Zifan Shi; Yinghao Xu; Yujun Shen; Deli Zhao; Qifeng Chen; Dit-Yan Yeung; |
| 540 | Accelerating Sparse Convolution for Efficient Neural Network Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an algorithm-software co-designed sparse convolution based on a novel out-vector-wise (OVW) sparse pattern. |
Yijun Tan; Kai Han; Kang Zhao; Xianzhi Yu; Zidong Du; Yunhe Wang; Jun Yao; Yunji Chen; |
| 541 | Exploiting The Relationship Between Kendall’s Rank Correlation and Cosine Similarity for Attribution Protection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we first show that the expected Kendall’s rank correlation is positively correlated to cosine similarity and then indicate that the direction of attribution is the key to attribution robustness. Based on these findings, we explore the vector space of attribution to explain the shortcomings of attribution defense methods using $\ell_p$ norm and propose integrated gradient regularizer (IGR), which maximizes the cosine similarity between natural and perturbed attributions. |
Fan Wang; Adams Wai Kin Kong; |
| 542 | Improved Fine-Tuning By Better Leveraging Pre-Training Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose to select and use pre-training data in the fine-tuning stage motivated by our theoretical analysis. |
Ziquan Liu; Yi Xu; Yuanhong Xu; Qi Qian; Hao Li; Xiangyang Ji; Antoni Chan; Rong Jin;</ |