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Most Influential NIPS Papers (2024-05)

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The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyzes all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2024-05)

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TABLE 1: Most Influential NIPS Papers (2024-05)

Year Rank Paper Author(s)
2023 1 Visual Instruction Tuning
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Highlight: We present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and an LLM for general-purpose visual and language understanding.		 Haotian Liu; Chunyuan Li; Qingyang Wu; Yong Jae Lee;
2023 2 Toolformer: Language Models Can Teach Themselves to Use Tools
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Highlight: In this paper, we show that LMs can teach themselves to *use external tools* via simple APIs and achieve the best of both worlds.		 TIMO SCHICK et. al.
2023 3 QLoRA: Efficient Finetuning of Quantized LLMs
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Highlight: We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance.		 Tim Dettmers; Artidoro Pagnoni; Ari Holtzman; Luke Zettlemoyer;
2023 4 Direct Preference Optimization: Your Language Model Is Secretly A Reward Model
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Highlight: However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data.		 RAFAEL RAFAILOV et. al.
2023 5 InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
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Highlight: In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pretrained BLIP-2 models.		 WENLIANG DAI et. al.
2023 6 Tree of Thoughts: Deliberate Problem Solving with Large Language Models
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Highlight: This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving.		 SHUNYU YAO et. al.
2023 7 Self-Refine: Iterative Refinement with Self-Feedback
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Highlight: Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.		 AMAN MADAAN et. al.
2023 8 HuggingGPT: Solving AI Tasks with ChatGPT and Its Friends in Hugging Face
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Highlight: Considering large language models (LLMs) have exhibited exceptional ability in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks and language could be a generic interface to empower this. Based on this philosophy, we present HuggingGPT, a framework that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks.		 YONGLIANG SHEN et. al.
2023 9 Reflexion: Language Agents with Verbal Reinforcement Learning
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Highlight: We propose Reflexion, a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback.		 Noah Shinn; Federico Cassano; Ashwin Gopinath; Karthik Narasimhan; Shunyu Yao;
2023 10 LIMA: Less Is More for Alignment
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Highlight: Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling		 CHUNTING ZHOU et. al.
2023 11 ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
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Highlight: In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present *variational score distillation* (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation.		 ZHENGYI WANG et. al.
2023 12 Language Is Not All You Need: Aligning Perception with Language Models
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Highlight: In this work, we introduce KOSMOS-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot).		 SHAOHAN HUANG et. al.
2023 13 OpenAssistant Conversations – Democratizing Large Language Model Alignment
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Highlight: In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 complete and fully annotated conversation trees.		 ANDREAS KÖPF et. al.
2023 14 Jailbroken: How Does LLM Safety Training Fail?
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Highlight: Large language models trained for safety and harmlessness remain susceptible to adversarial misuse, as evidenced by the prevalence of �jailbreak� attacks on early releases of ChatGPT that elicit undesired behavior. Going beyond recognition of the issue, we investigate why such attacks succeed and how they can be created.		 Alexander Wei; Nika Haghtalab; Jacob Steinhardt;
2023 15 Mathematical Capabilities of ChatGPT
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Highlight: We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology.		 SIMON FRIEDER et. al.
2022 1 Training Language Models to Follow Instructions with Human Feedback
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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 et. al.
2022 2 Chain of Thought Prompting Elicits Reasoning in Large Language Models
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Highlight: We explore how generating a chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning.		 JASON WEI et. al.
2022 3 Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
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Highlight: We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.		 CHITWAN SAHARIA et. al.
2022 4 Large Language Models Are Zero-Shot Reasoners
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Highlight: We propose a single zero-shot prompt that elicits effective chain of thought reasoning across diverse benchmarks that require multi-step thinking.		 Takeshi Kojima; Shixiang (Shane) Gu; Machel Reid; Yutaka Matsuo; Yusuke Iwasawa;
2022 5 Flamingo: A Visual Language Model for Few-Shot Learning
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Highlight: Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability.		 JEAN-BAPTISTE ALAYRAC et. al.
2022 6 LAION-5B: An Open Large-scale Dataset for Training Next Generation Image-text Models
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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 et. al.
2022 7 Elucidating The Design Space of Diffusion-Based Generative Models
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Highlight: We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices.		 Tero Karras; Miika Aittala; Timo Aila; Samuli Laine;
2022 8 FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
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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;
2022 9 Video Diffusion Models
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Highlight: To generate long and higher resolution videos we introduce a new conditional sampling technique for spatial and temporal video extension that performs better than previously proposed methods.		 JONATHAN HO et. al.
2022 10 DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
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Highlight: In this work, we propose an exact formulation of the solution of diffusion ODEs.		 CHENG LU et. al.
2022 11 The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
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Highlight: We demonstrate PPO’s effectiveness in popular multi-agent benchmarks and analyze its properties and implementation details through empirical studies.		 CHAO YU et. al.
2022 12 VideoMAE: Masked Autoencoders Are Data-Efficient Learners for Self-Supervised Video Pre-Training
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Highlight: In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP).		 Zhan Tong; Yibing Song; Jue Wang; Limin Wang;
2022 13 Locating and Editing Factual Associations in GPT
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Highlight: We locate and edit the mechanisms underlying factual association within the activations and weights of large pretrained GPT models.		 Kevin Meng; David Bau; Alex Andonian; Yonatan Belinkov;
2022 14 Diffusion-LM Improves Controllable Text Generation
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Highlight: While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic structure). To address this challenge, we develop a new non-autoregressive language model based on continuous diffusions that we call Diffusion-LM.		 Xiang Li; John Thickstun; Ishaan Gulrajani; Percy Liang; Tatsunori Hashimoto;
2022 15 Solving Quantitative Reasoning Problems with Language Models
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Highlight: We train a large Transformer language model on mathematical data and achieve strong performance on quantitative reasoning tasks, including state of the art performance on the MATH dataset.		 AITOR LEWKOWYCZ et. al.
2021 1 Diffusion Models Beat GANs on Image Synthesis
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Highlight: We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models.		 Prafulla Dhariwal; Alexander Nichol;
2021 2 SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
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Highlight: We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perceptron (MLP) decoders.		 ENZE XIE et. al.
2021 3 MLP-Mixer: An All-MLP Architecture for Vision
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Highlight: In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary.		 ILYA O. TOLSTIKHIN et. al.
2021 4 Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation
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Highlight: In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning.		 JUNNAN LI et. al.
2021 5 Alias-Free Generative Adversarial Networks
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Highlight: Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process.		 TERO KARRAS et. al.
2021 6 Transformer in Transformer
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Highlight: In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT).		 KAI HAN et. al.
2021 7 NeuS: Learning Neural Implicit Surfaces By Volume Rendering for Multi-view Reconstruction
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Highlight: We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs.		 PENG WANG et. al.
2021 8 Decision Transformer: Reinforcement Learning Via Sequence Modeling
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Highlight: We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem.		 LILI CHEN et. al.
2021 9 Per-Pixel Classification Is Not All You Need for Semantic Segmentation
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Highlight: Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction.		 Bowen Cheng; Alex Schwing; Alexander Kirillov;
2021 10 Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
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Highlight: This paper studies the long-term forecasting problem of time series.		 haixu wu; Jiehui Xu; Jianmin Wang; Mingsheng Long;
2021 11 CoAtNet: Marrying Convolution and Attention for All Data Sizes
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Highlight: In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias.		 Zihang Dai; Hanxiao Liu; Quoc Le; Mingxing Tan;
2021 12 Twins: Revisiting The Design of Spatial Attention in Vision Transformers
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Highlight: In this work, we revisit the design of the spatial attention and demonstrate that a carefully devised yet simple spatial attention mechanism performs favorably against the state-of-the-art schemes.		 XIANGXIANG CHU et. al.
2021 13 Volume Rendering of Neural Implicit Surfaces
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Highlight: The goal of this paper is to improve geometry representation and reconstruction in neural volume rendering.		 Lior Yariv; Jiatao Gu; Yoni Kasten; Yaron Lipman;
2021 14 Do Vision Transformers See Like Convolutional Neural Networks?
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Highlight: Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers.		 Maithra Raghu; Thomas Unterthiner; Simon Kornblith; Chiyuan Zhang; Alexey Dosovitskiy;
2021 15 Early Convolutions Help Transformers See Better
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Highlight: In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural networks are easier to optimize. Why is this the case? In this work, we conjecture that the issue lies with the patchify stem of ViT models, which is implemented by a stride-p p×p convolution (p = 16 by default) applied to the input image.		 TETE XIAO et. al.
2020 1 Language Models Are Few-Shot Learners
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Highlight: Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.		 TOM BROWN et. al.
2020 2 Denoising Diffusion Probabilistic Models
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Highlight: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.		 Jonathan Ho; Ajay Jain; Pieter Abbeel;
2020 3 Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
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Highlight: We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.		 Alexei Baevski; Yuhao Zhou; Abdel-rahman Mohamed; Michael Auli;
2020 4 Decision-Making with Auto-Encoding Variational Bayes
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Highlight: Motivated by these theoretical results, we propose learning several approximate proposals for the best model and combining them using multiple importance sampling for decision-making.		 Romain Lopez; Pierre Boyeau; Nir Yosef; Michael Jordan; Jeffrey Regier;
2020 5 Supervised Contrastive Learning
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Highlight: In this work, we extend the self-supervised batch contrastive approach to the fully-supervised setting, allowing us to effectively leverage label information.		 PRANNAY KHOSLA et. al.
2020 6 Unsupervised Learning of Visual Features By Contrasting Cluster Assignments
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Highlight: In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons.		 MATHILDE CARON et. al.
2020 7 RandAugment: Practical Automated Data Augmentation with A Reduced Search Space
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Highlight: In this work, we rethink the process of designing automated data augmentation strategies.		 Ekin Dogus Cubuk; Barret Zoph; Jon Shlens; Quoc Le;
2020 8 FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
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Highlight: In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods.		 KIHYUK SOHN et. al.
2020 9 Unsupervised Data Augmentation for Consistency Training
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Highlight: In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.		 Qizhe Xie; Zihang Dai; Eduard Hovy; Thang Luong; Quoc Le;
2020 10 Open Graph Benchmark: Datasets for Machine Learning on Graphs
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Highlight: We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.		 WEIHUA HU et. al.
2020 11 Big Self-Supervised Models Are Strong Semi-Supervised Learners
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Highlight: A key ingredient of our approach is the use of big (deep and wide) networks during pretraining and fine-tuning. We find that, the fewer the labels, the more this approach (task-agnostic use of unlabeled data) benefits from a bigger network.		 Ting Chen; Simon Kornblith; Kevin Swersky; Mohammad Norouzi; Geoffrey E. Hinton;
2020 12 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
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Highlight: We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever.		 PATRICK LEWIS et. al.
2020 13 Implicit Neural Representations with Periodic Activation Functions
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Highlight: We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or SIRENs, are ideally suited for representing complex natural signals and their derivatives.		 Vincent Sitzmann; Julien Martel; Alexander Bergman; David Lindell; Gordon Wetzstein;
2020 14 Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
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Highlight: We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities.		 MATTHEW TANCIK et. al.
2020 15 Big Bird: Transformers for Longer Sequences
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Highlight: To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.		 MANZIL ZAHEER et. al.
2019 1 PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Highlight: In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture.		 ADAM PASZKE et. al.
2019 2 XLNet: Generalized Autoregressive Pretraining for Language Understanding
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Highlight: In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation.		 ZHILIN YANG et. al.
2019 3 ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
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Highlight: We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language.		 Jiasen Lu; Dhruv Batra; Devi Parikh; Stefan Lee;
2019 4 MixMatch: A Holistic Approach to Semi-Supervised Learning
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Highlight: In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that guesses low-entropy labels for data-augmented unlabeled examples and mixes labeled and unlabeled data using MixUp.		 DAVID BERTHELOT et. al.
2019 5 Cross-lingual Language Model Pretraining
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Highlight: In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining.		 Alexis CONNEAU; Guillaume Lample;
2019 6 Generative Modeling By Estimating Gradients of The Data Distribution
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Highlight: We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.		 Yang Song; Stefano Ermon;
2019 7 SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
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Highlight: In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard.		 ALEX WANG et. al.
2019 8 Adversarial Examples Are Not Bugs, They Are Features
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Highlight: We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features (derived from patterns in the data distribution) that are highly predictive, yet brittle and (thus) incomprehensible to humans.		 ANDREW ILYAS et. al.
2019 9 When Does Label Smoothing Help?
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Highlight: We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters.		 Rafael M�ller; Simon Kornblith; Geoffrey E. Hinton;
2019 10 Deep Leakage from Gradients
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Highlight: However, in this paper, we show that we can obtain the private training set from the publicly shared gradients.		 Ligeng Zhu; Zhijian Liu; Song Han;
2019 11 Unified Language Model Pre-training for Natural Language Understanding and Generation
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Highlight: This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.		 LI DONG et. al.
2019 12 Learning Representations By Maximizing Mutual Information Across Views
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Highlight: We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context.		 Philip Bachman; R Devon Hjelm; William Buchwalter;
2019 13 Generating Diverse High-Fidelity Images with VQ-VAE-2
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Highlight: We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.		 Ali Razavi; Aaron van den Oord; Oriol Vinyals;
2019 14 GPipe: Efficient Training of Giant Neural Networks Using Pipeline Parallelism
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Highlight: To address the need for efficient and task-independent model parallelism, we introduce TensorPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers.		 YANPING HUANG et. al.
2019 15 Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
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Highlight: Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios.		 Kaidi Cao; Colin Wei; Adrien Gaidon; Nikos Arechiga; Tengyu Ma;
2018 1 Neural Ordinary Differential Equations
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Highlight: We introduce a new family of deep neural network models.		 Tian Qi Chen; Yulia Rubanova; Jesse Bettencourt; David K. Duvenaud;
2018 2 Glow: Generative Flow with Invertible 1×1 Convolutions
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Highlight: In this paper we propose Glow, a simple type of generative flow using invertible 1×1 convolution.		 Durk P. Kingma; Prafulla Dhariwal;
2018 3 Neural Tangent Kernel: Convergence and Generalization in Neural Networks
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Highlight: We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function (which maps input vectors to output vectors) follows the so-called kernel gradient associated with a new object, which we call the Neural Tangent Kernel (NTK).		 Arthur Jacot; Franck Gabriel; Clement Hongler;
2018 4 PointCNN: Convolution On X-Transformed Points
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Highlight: We present a simple and general framework for feature learning from point cloud.		 YANGYAN LI et. al.
2018 5 Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
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Highlight: Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE.		 Zhilu Zhang; Mert Sabuncu;
2018 6 Conditional Adversarial Domain Adaptation
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Highlight: In this paper, we present conditional adversarial domain adaptation, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions.		 Mingsheng Long; ZHANGJIE CAO; Jianmin Wang; Michael I. Jordan;
2018 7 Hierarchical Graph Representation Learning with Differentiable Pooling
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Highlight: Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion.		 ZHITAO YING et. al.
2018 8 Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
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Highlight: Therefore in this paper, we propose a new deep learning paradigm called ”Co-teaching” for combating with noisy labels.		 BO HAN et. al.
2018 9 Sanity Checks for Saliency Maps
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Highlight: In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide.		 JULIUS ADEBAYO et. al.
2018 10 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
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Highlight: In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier.		 Kimin Lee; Kibok Lee; Honglak Lee; Jinwoo Shin;
2018 11 Link Prediction Based on Graph Neural Networks
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Highlight: In this paper, we study this heuristic learning paradigm for link prediction.		 Muhan Zhang; Yixin Chen;
2018 12 Visualizing The Loss Landscape of Neural Nets
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Highlight: In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods.		 Hao Li; Zheng Xu; Gavin Taylor; Christoph Studer; Tom Goldstein;
2018 13 TADAM: Task Dependent Adaptive Metric for Improved Few-shot Learning
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Highlight: In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms.		 Boris Oreshkin; Pau Rodr�guez L�pez; Alexandre Lacoste;
2018 14 How Does Batch Normalization Help Optimization?
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Highlight: In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm.		 Shibani Santurkar; Dimitris Tsipras; Andrew Ilyas; Aleksander Madry;
2018 15 Deep Reinforcement Learning in A Handful of Trials Using Probabilistic Dynamics Models
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Highlight: In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models.		 Kurtland Chua; Roberto Calandra; Rowan McAllister; Sergey Levine;
2017 1 Attention Is All You Need
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Highlight: We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train.		 ASHISH VASWANI et. al.
2017 2 A Unified Approach to Interpreting Model Predictions
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Highlight: To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations).		 Scott M. Lundberg; Su-In Lee;
2017 3 Inductive Representation Learning on Large Graphs
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Highlight: Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings.		 Will Hamilton; Zhitao Ying; Jure Leskovec;
2017 4 GANs Trained By A Two Time-Scale Update Rule Converge to A Local Nash Equilibrium
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Highlight: We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions.		 Martin Heusel; Hubert Ramsauer; Thomas Unterthiner; Bernhard Nessler; Sepp Hochreiter;
2017 5 Improved Training of Wasserstein GANs
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Highlight: We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input.		 Ishaan Gulrajani; Faruk Ahmed; Martin Arjovsky; Vincent Dumoulin; Aaron C. Courville;
2017 6 PointNet++: Deep Hierarchical Feature Learning on Point Sets in A Metric Space
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Highlight: In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales.		 Charles Ruizhongtai Qi; Li Yi; Hao Su; Leonidas J. Guibas;
2017 7 LightGBM: A Highly Efficient Gradient Boosting Decision Tree
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Highlight: To tackle this problem, we propose two novel techniques: \emph{Gradient-based One-Side Sampling} (GOSS) and \emph{Exclusive Feature Bundling} (EFB).		 GUOLIN KE et. al.
2017 8 Prototypical Networks for Few-shot Learning
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Highlight: We propose Prototypical Networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.		 Jake Snell; Kevin Swersky; Richard Zemel;
2017 9 Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles
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Highlight: We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates.		 Balaji Lakshminarayanan; Alexander Pritzel; Charles Blundell;
2017 10 Dynamic Routing Between Capsules
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Highlight: We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters.		 Sara Sabour; Nicholas Frosst; Geoffrey E. Hinton;
2017 11 What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
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Highlight: For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty.		 Alex Kendall; Yarin Gal;
2017 12 Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
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Highlight: We explore deep reinforcement learning methods for multi-agent domains.		 RYAN LOWE et. al.
2017 13 Neural Discrete Representation Learning
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Highlight: In this paper, we propose a simple yet powerful generative model that learns such discrete representations.		 Aaron van den Oord; Oriol Vinyals; koray kavukcuoglu;
2017 14 Unsupervised Image-to-Image Translation Networks
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Highlight: To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs.		 Ming-Yu Liu; Thomas Breuel; Jan Kautz;
2017 15 Mean Teachers Are Better Role Models: Weight-averaged Consistency Targets Improve Semi-supervised Deep Learning Results
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Highlight: To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions.		 Antti Tarvainen; Harri Valpola;
2016 1 Improved Techniques for Training GANs
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Highlight: We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.		 TIM SALIMANS et. al.
2016 2 Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
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Highlight: In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words’ embedding, represented by graphs.		 Micha�l Defferrard; Xavier Bresson; Pierre Vandergheynst;
2016 3 Matching Networks for One Shot Learning
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Highlight: In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories.		 Oriol Vinyals; Charles Blundell; Timothy Lillicrap; koray kavukcuoglu; Daan Wierstra;
2016 4 R-FCN: Object Detection Via Region-based Fully Convolutional Networks
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Highlight: We present region-based, fully convolutional networks for accurate and efficient object detection.		 Jifeng Dai; Yi Li; Kaiming He; Jian Sun;
2016 5 InfoGAN: Interpretable Representation Learning By Information Maximizing Generative Adversarial Nets
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Highlight: This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.		 XI CHEN et. al.
2016 6 Equality of Opportunity in Supervised Learning
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Highlight: We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features.		 Moritz Hardt; Eric Price; Nati Srebro;
2016 7 Man Is to Computer Programmer As Woman Is to Homemaker? Debiasing Word Embeddings
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Highlight: This raises concerns because their widespread use, as we describe, often tends to amplify these biases.		 Tolga Bolukbasi; Kai-Wei Chang; James Y. Zou; Venkatesh Saligrama; Adam T. Kalai;
2016 8 Generative Adversarial Imitation Learning
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Highlight: We propose a new general framework for directly extracting a policy from data as if it were obtained by reinforcement learning following inverse reinforcement learning.		 Jonathan Ho; Stefano Ermon;
2016 9 Conditional Image Generation with PixelCNN Decoders
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Highlight: This work explores conditional image generation with a new image density model based on the PixelCNN architecture.		 AARON VAN DEN OORD et. al.
2016 10 Learning Structured Sparsity in Deep Neural Networks
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Highlight: In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs.		 Wei Wen; Chunpeng Wu; Yandan Wang; Yiran Chen; Hai Li;
2016 11 Learning to Learn By Gradient Descent By Gradient Descent
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Highlight: In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way.		 MARCIN ANDRYCHOWICZ et. al.
2016 12 Improved Deep Metric Learning with Multi-class N-pair Loss Objective
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Highlight: In this paper, we propose to address this problem with a new metric learning objective called multi-class N-pair loss.		 Kihyuk Sohn;
2016 13 Learning A Probabilistic Latent Space of Object Shapes Via 3D Generative-Adversarial Modeling
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Highlight: We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets.		 Jiajun Wu; Chengkai Zhang; Tianfan Xue; Bill Freeman; Josh Tenenbaum;
2016 14 Binarized Neural Networks
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Highlight: We introduce a method to train Binarized Neural Networks (BNNs) – neural networks with binary weights and activations at run-time.		 Itay Hubara; Matthieu Courbariaux; Daniel Soudry; Ran El-Yaniv; Yoshua Bengio;
2016 15 Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
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Highlight: We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction.		 Tim Salimans; Durk P. Kingma;
2015 1 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Highlight: In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.		 Shaoqing Ren; Kaiming He; Ross Girshick; Jian Sun;
2015 2 Spatial Transformer Networks
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Highlight: In this work we introduce a new learnable module, theSpatial Transformer, which explicitly allows the spatial manipulation ofdata within the network.		 Max Jaderberg; Karen Simonyan; Andrew Zisserman; koray kavukcuoglu;
2015 3 Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
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Highlight: In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences.		 XINGJIAN SHI et. al.
2015 4 Learning Both Weights and Connections for Efficient Neural Network
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Highlight: To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections.		 Song Han; Jeff Pool; John Tran; William Dally;
2015 5 Character-level Convolutional Networks for Text Classification
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Highlight: This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results.		 Xiang Zhang; Junbo Zhao; Yann LeCun;
2015 6 Teaching Machines to Read and Comprehend
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Highlight: In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data.		 KARL MORITZ HERMANN et. al.
2015 7 Convolutional Networks on Graphs for Learning Molecular Fingerprints
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Highlight: We introduce a convolutional neural network that operates directly on graphs.These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints.We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.		 DAVID K. DUVENAUD et. al.
2015 8 BinaryConnect: Training Deep Neural Networks with Binary Weights During Propagations
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Highlight: We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated.		 Matthieu Courbariaux; Yoshua Bengio; Jean-Pierre David;
2015 9 Pointer Networks
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Highlight: We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that arediscrete tokens corresponding to positions in an input sequence.Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing Machines,because the number of target classes in eachstep of the output depends on the length of the input, which is variable.Problems such as sorting variable sized sequences, and various combinatorialoptimization problems belong to this class.		 Oriol Vinyals; Meire Fortunato; Navdeep Jaitly;
2015 10 Learning Structured Output Representation Using Deep Conditional Generative Models
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Highlight: In this work, we develop a scalable deep conditional generative model for structured output variables using Gaussian latent variables.		 Kihyuk Sohn; Honglak Lee; Xinchen Yan;
2015 11 Attention-Based Models for Speech Recognition
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Highlight: We offer a qualitative explanation of this failure and propose a novel and generic method of adding location-awareness to the attention mechanism to alleviate this issue.		 Jan K. Chorowski; Dzmitry Bahdanau; Dmitriy Serdyuk; Kyunghyun Cho; Yoshua Bengio;
2015 12 End-To-End Memory Networks
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Highlight: We introduce a neural network with a recurrent attention model over a possibly large external memory.		 Sainbayar Sukhbaatar; arthur szlam; Jason Weston; Rob Fergus;
2015 13 Skip-Thought Vectors
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Highlight: We describe an approach for unsupervised learning of a generic, distributed sentence encoder.		 RYAN KIROS et. al.
2015 14 Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
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Highlight: We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead.		 Samy Bengio; Oriol Vinyals; Navdeep Jaitly; Noam Shazeer;
2015 15 Training Very Deep Networks
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Highlight: Here we introduce a new architecture designed to overcome this.		 Rupesh K. Srivastava; Klaus Greff; J�rgen Schmidhuber;
2014 1 Sequence to Sequence Learning with Neural Networks
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Highlight: In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure.		 Ilya Sutskever; Oriol Vinyals; Quoc V. Le;
2014 2 How Transferable Are Features in Deep Neural Networks?
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Highlight: In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results.		 Jason Yosinski; Jeff Clune; Yoshua Bengio; Hod Lipson;
2014 3 Two-Stream Convolutional Networks for Action Recognition in Videos
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Highlight: Our contribution is three-fold.		 Karen Simonyan; Andrew Zisserman;
2014 4 Depth Map Prediction from A Single Image Using A Multi-Scale Deep Network
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Highlight: In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally.		 David Eigen; Christian Puhrsch; Rob Fergus;
2014 5 Recurrent Models of Visual Attention
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Highlight: We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution.		 Volodymyr Mnih; Nicolas Heess; Alex Graves; koray kavukcuoglu;
2014 6 Learning Deep Features for Scene Recognition Using Places Database
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Highlight: We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes.		 Bolei Zhou; Agata Lapedriza; Jianxiong Xiao; Antonio Torralba; Aude Oliva;
2014 7 Semi-supervised Learning with Deep Generative Models
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Highlight: We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones.		 Durk P. Kingma; Shakir Mohamed; Danilo Jimenez Rezende; Max Welling;
2014 8 Generative Adversarial Nets
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Highlight: We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G.		 IAN GOODFELLOW et. al.
2014 9 Deep Learning Face Representation By Joint Identification-Verification
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Highlight: In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision.		 Yi Sun; Yuheng Chen; Xiaogang Wang; Xiaoou Tang;
2014 10 Do Deep Nets Really Need to Be Deep?
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Highlight: In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models.		 Jimmy Ba; Rich Caruana;
2014 11 Neural Word Embedding As Implicit Matrix Factorization
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Highlight: We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs, shifted by a global constant.		 Omer Levy; Yoav Goldberg;
2014 12 SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
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Highlight: In this work we introduce a new fast incremental gradient method SAGA, in the spirit of SAG, SDCA, MISO and SVRG.		 Aaron Defazio; Francis Bach; Simon Lacoste-Julien;
2014 13 Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
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Highlight: We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks.		 Emily L. Denton; Wojciech Zaremba; Joan Bruna; Yann LeCun; Rob Fergus;
2014 14 Joint Training of A Convolutional Network and A Graphical Model for Human Pose Estimation
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Highlight: This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field.		 Jonathan J. Tompson; Arjun Jain; Yann LeCun; Christoph Bregler;
2014 15 Convolutional Neural Network Architectures for Matching Natural Language Sentences
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Highlight: As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech.		 Baotian Hu; Zhengdong Lu; Hang Li; Qingcai Chen;
2013 1 Distributed Representations of Words and Phrases and Their Compositionality
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Highlight: In this paper we present several improvements that make the Skip-gram model more expressive and enable it to learn higher quality vectors more rapidly.		 Tomas Mikolov; Ilya Sutskever; Kai Chen; Greg S. Corrado; Jeff Dean;
2013 2 Translating Embeddings for Modeling Multi-relational Data
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Highlight: Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases.		 Antoine Bordes; Nicolas Usunier; Alberto Garcia-Duran; Jason Weston; Oksana Yakhnenko;
2013 3 Sinkhorn Distances: Lightspeed Computation of Optimal Transport
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Highlight: We propose in this work a new family of optimal transportation distances that look at transportation problems from a maximum-entropy perspective.		 Marco Cuturi;
2013 4 DeViSE: A Deep Visual-Semantic Embedding Model
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Highlight: In this paper we present a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text.		 ANDREA FROME et. al.
2013 5 Accelerating Stochastic Gradient Descent Using Predictive Variance Reduction
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Highlight: To remedy this problem, we introduce an explicit variance reduction method for stochastic gradient descent which we call stochastic variance reduced gradient (SVRG).		 Rie Johnson; Tong Zhang;
2013 6 Reasoning With Neural Tensor Networks for Knowledge Base Completion
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Highlight: The goal of this paper is to develop a more powerful neural network model suitable for inference over these relationships.		 Richard Socher; Danqi Chen; Christopher D. Manning; Andrew Ng;
2013 7 Deep Neural Networks for Object Detection
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Highlight: In this paper we go one step further and address the problem of object detection — not only classifying but also precisely localizing objects of various classes using DNNs.		 Christian Szegedy; Alexander Toshev; Dumitru Erhan;
2013 8 Zero-Shot Learning Through Cross-Modal Transfer
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Highlight: This work introduces a model that can recognize objects in images even if no training data is available for the object class.		 Richard Socher; Milind Ganjoo; Christopher D. Manning; Andrew Ng;
2013 9 Predicting Parameters in Deep Learning
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Highlight: We demonstrate that there is significant redundancy in the parameterization of several deep learning models.		 Misha Denil; Babak Shakibi; Laurent Dinh; Marc’Aurelio Ranzato; Nando de Freitas;
2013 10 Deep Content-based Music Recommendation
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Highlight: In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data.		 Aaron van den Oord; Sander Dieleman; Benjamin Schrauwen;
2013 11 Learning with Noisy Labels
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Highlight: In this paper, we theoretically study the problem of binary classification in the presence of random classification noise — the learner, instead of seeing the true labels, sees labels that have independently been flipped with some small probability.		 Nagarajan Natarajan; Inderjit S. Dhillon; Pradeep K. Ravikumar; Ambuj Tewari;
2013 12 Learning A Deep Compact Image Representation for Visual Tracking
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Highlight: In this paper, we study the challenging problem of tracking the trajectory of a moving object in a video with possibly very complex background.		 Naiyan Wang; Dit-Yan Yeung;
2013 13 Understanding Variable Importances in Forests of Randomized Trees
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Highlight: In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally randomized trees in asymptotic sample and ensemble size conditions.		 Gilles Louppe; Louis Wehenkel; Antonio Sutera; Pierre Geurts;
2013 14 More Effective Distributed ML Via A Stale Synchronous Parallel Parameter Server
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Highlight: We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees.		 QIRONG HO et. al.
2013 15 Multi-Task Bayesian Optimization
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Highlight: In this paper, we explore whether it is possible to transfer the knowledge gained from previous optimizations to new tasks in order to find optimal hyperparameter settings more efficiently.		 Kevin Swersky; Jasper Snoek; Ryan P. Adams;
2012 1 ImageNet Classification with Deep Convolutional Neural Networks
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Highlight: To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.		 Alex Krizhevsky; Ilya Sutskever; Geoffrey E. Hinton;
2012 2 Practical Bayesian Optimization of Machine Learning Algorithms
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Highlight: In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP).		 Jasper Snoek; Hugo Larochelle; Ryan P. Adams;
2012 3 Large Scale Distributed Deep Networks
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Highlight: In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores.		 JEFFREY DEAN et. al.
2012 4 Learning to Discover Social Circles in Ego Networks
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Highlight: We pose the problem as a node clustering problem on a user’s ego-network, a network of connections between her friends. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter for all of which we obtain hand-labeled ground-truth data.		 Jure Leskovec; Julian J. Mcauley;
2012 5 Multimodal Learning with Deep Boltzmann Machines
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Highlight: We propose a Deep Boltzmann Machine for learning a generative model of multimodal data.		 Nitish Srivastava; Russ R. Salakhutdinov;
2012 6 Image Denoising and Inpainting with Deep Neural Networks
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Highlight: We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA).		 Junyuan Xie; Linli Xu; Enhong Chen;
2012 7 Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
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Highlight: We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images.		 Dan Ciresan; Alessandro Giusti; Luca M. Gambardella; J�rgen Schmidhuber;
2012 8 Hamming Distance Metric Learning
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Highlight: Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity.		 Mohammad Norouzi; David J. Fleet; Russ R. Salakhutdinov;
2012 9 Convolutional-Recursive Deep Learning for 3D Object Classification
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 Richard Socher; Brody Huval; Bharath Bath; Christopher D. Manning; Andrew Y. Ng;
2012 10 Optimal Kernel Choice for Large-scale Two-sample Tests
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Abstract: Given samples from distributions $p$ and $q$, a two-sample test determines whether to reject the null hypothesis that $p=q$, based on the value of a test statistic measuring the …		 ARTHUR GRETTON et. al.
2012 11 Communication-Efficient Algorithms for Statistical Optimization
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Highlight: We study two communication-efficient algorithms for distributed statistical optimization on large-scale data.		 Yuchen Zhang; Martin J. Wainwright; John C. Duchi;
2012 12 A Simple and Practical Algorithm for Differentially Private Data Release
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Highlight: We present a new algorithm for differentially private data release, based on a simple combination of the Exponential Mechanism with the Multiplicative Weights update rule.		 Moritz Hardt; Katrina Ligett; Frank Mcsherry;
2012 13 A Latent Factor Model for Highly Multi-relational Data
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Highlight: In this paper, we propose a method for modeling large multi-relational datasets, with possibly thousands of relations.		 Rodolphe Jenatton; Nicolas L. Roux; Antoine Bordes; Guillaume R. Obozinski;
2012 14 Learning from The Wisdom of Crowds By Minimax Entropy
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Highlight: We propose a minimax entropy principle to improve the quality of these labels.		 Dengyong Zhou; Sumit Basu; Yi Mao; John C. Platt;
2012 15 Variational Inference for Crowdsourcing
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Highlight: We approach this problem by transforming it into a standard inference problem in graphical models, and applying approximate variational methods, including belief propagation (BP) and mean field (MF).		 Qiang Liu; Jian Peng; Alexander T. Ihler;
2011 1 Algorithms for Hyper-Parameter Optimization
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Highlight: We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs).		 James S. Bergstra; R�mi Bardenet; Yoshua Bengio; Bal�zs K�gl;
2011 2 Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
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Highlight: This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented *without any locking*.		 Benjamin Recht; Christopher Re; Stephen Wright; Feng Niu;
2011 3 Improved Algorithms for Linear Stochastic Bandits
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Highlight: We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem.		 Yasin Abbasi-yadkori; D�vid P�l; Csaba Szepesv�ri;
2011 4 Practical Variational Inference for Neural Networks
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Highlight: This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks.		 Alex Graves;
2011 5 An Empirical Evaluation of Thompson Sampling
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Highlight: We present here some empirical results using Thompson sampling on simulated and real data, and show that it is highly competitive.		 Olivier Chapelle; Lihong Li;
2011 6 Im2Text: Describing Images Using 1 Million Captioned Photographs
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Highlight: We develop and demonstrate automatic image description methods using a large captioned photo collection.		 Vicente Ordonez; Girish Kulkarni; Tamara L. Berg;
2011 7 Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
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Highlight: To address this issue, we propose a linearized ADM (LADM) method by linearizing the quadratic penalty term and adding a proximal term when solving the subproblems.		 Zhouchen Lin; Risheng Liu; Zhixun Su;
2011 8 Co-regularized Multi-view Spectral Clustering
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Highlight: We propose a spectral clustering framework that achieves this goal by co-regularizing the clustering hypotheses, and propose two co-regularization schemes to accomplish this.		 Abhishek Kumar; Piyush Rai; Hal Daume;
2011 9 Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
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 Richard Socher; Eric H. Huang; Jeffrey Pennin; Christopher D. Manning; Andrew Y. Ng;
2011 10 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
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Highlight: We provide a non-asymptotic analysis of the convergence of two well-known algorithms, stochastic gradient descent (a.k.a.~Robbins-Monro algorithm) as well as a simple modification where iterates are averaged (a.k.a.~Polyak-Ruppert averaging).		 Eric Moulines; Francis R. Bach;
2011 11 Distributed Delayed Stochastic Optimization
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Highlight: Our main contribution is to show that for smooth stochastic problems, the delays are asymptotically negligible.		 Alekh Agarwal; John C. Duchi;
2011 12 Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
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Highlight: We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal-gradient methods, where an error is present in the calculation of the gradient of the smooth term or in the proximity operator with respect to the second term.		 Mark Schmidt; Nicolas L. Roux; Francis R. Bach;
2011 13 High-dimensional Regression with Noisy and Missing Data: Provable Guarantees with Non-convexity
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Highlight: We study these issues in the context of high-dimensional sparse linear regression, and propose novel estimators for the cases of noisy, missing, and/or dependent data.		 Po-ling Loh; Martin J. Wainwright;
2011 14 Iterative Learning for Reliable Crowdsourcing Systems
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Highlight: In this paper, we consider a general model of such rowdsourcing tasks, and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability.		 David R. Karger; Sewoong Oh; Devavrat Shah;
2011 15 Semantic Labeling of 3D Point Clouds for Indoor Scenes
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Highlight: In this paper, we use this data to build 3D point clouds of full indoor scenes such as an office and address the task of semantic labeling of these 3D point clouds.		 Hema S. Koppula; Abhishek Anand; Thorsten Joachims; Ashutosh Saxena;
2010 1 Online Learning for Latent Dirichlet Allocation
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Highlight: We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA).		 Matthew Hoffman; Francis R. Bach; David M. Blei;
2010 2 Double Q-learning
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Highlight: We introduce an alternative way to approximate the maximum expected value for any set of random variables.		 Hado V. Hasselt;
2010 3 Parallelized Stochastic Gradient Descent
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Highlight: In this paper we present the first parallel stochastic gradient descent algorithm including a detailed analysis and experimental evidence.		 Martin Zinkevich; Markus Weimer; Lihong Li; Alex J. Smola;
2010 4 Self-Paced Learning for Latent Variable Models
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Highlight: To alleviate this problem, we build on the intuition that, rather than considering all samples simultaneously, the algorithm should be presented with the training data in a meaningful order that facilitates learning.		 M. P. Kumar; Benjamin Packer; Daphne Koller;
2010 5 Learning To Count Objects in Images
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Highlight: We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames.		 Victor Lempitsky; Andrew Zisserman;
2010 6 Monte-Carlo Planning in Large POMDPs
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Highlight: This paper introduces a Monte-Carlo algorithm for online planning in large POMDPs.		 David Silver; Joel Veness;
2010 7 The Multidimensional Wisdom of Crowds
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 Peter Welinder; Steve Branson; Pietro Perona; Serge J. Belongie;
2010 8 Robust PCA Via Outlier Pursuit
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Highlight: We present an efficient convex optimization-based algorithm we call Outlier Pursuit, that under some mild assumptions on the uncorrupted points (satisfied, e.g., by the standard generative assumption in PCA problems) recovers the *exact* optimal low-dimensional subspace, and identifies the corrupted points.		 Huan Xu; Constantine Caramanis; Sujay Sanghavi;
2010 9 Extended Bayesian Information Criteria for Gaussian Graphical Models
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Highlight: In this paper we establish the asymptotic consistency of an extended Bayesian information criterion for Gaussian graphical models in a scenario where both the number of variables p and the sample size n grow.		 Rina Foygel; Mathias Drton;
2010 10 Active Learning By Querying Informative and Representative Examples
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Highlight: We address this challenge by a principled approach, termed QUIRE, based on the min-max view of active learning.		 Sheng-jun Huang; Rong Jin; Zhi-Hua Zhou;
2010 11 Learning Convolutional Feature Hierarchies for Visual Recognition
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Highlight: We propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features.		 KORAY KAVUKCUOGLU et. al.
2010 12 Guaranteed Rank Minimization Via Singular Value Projection
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Highlight: In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization under affine constraints ARMP and show that SVP recovers the minimum rank solution for affine constraints that satisfy a Restricted Isometry Property} (RIP).		 Prateek Jain; Raghu Meka; Inderjit S. Dhillon;
2010 13 Learning to Combine Foveal Glimpses with A Third-order Boltzmann Machine
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Highlight: We describe a model based on a Boltzmann machine with third-order connections that can learn how to accumulate information about a shape over several fixations.		 Hugo Larochelle; Geoffrey E. Hinton;
2010 14 Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
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Highlight: In this paper, we present StARS: a new stability-based method for choosing the regularization parameter in high dimensional inference for undirected graphs.		 Han Liu; Kathryn Roeder; Larry Wasserman;
2010 15 Parametric Bandits: The Generalized Linear Case
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Highlight: We pro- pose a new optimistic, UCB-like, algorithm for non-linearly parameterized bandit problems using the Generalized Linear Model (GLM) framework.		 Sarah Filippi; Olivier Cappe; Aur�lien Garivier; Csaba Szepesv�ri;
2009 1 Reading Tea Leaves: How Humans Interpret Topic Models
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 Jonathan Chang; Sean Gerrish; Chong Wang; Jordan L. Boyd-graber; David M. Blei;
2009 2 Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices Via Convex Optimization
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Highlight: This paper considers the idealized “robust principal component analysis” problem of recovering a low rank matrix A from corrupted observations D = A + E. Here, the error entries E can be arbitrarily large (modeling grossly corrupted observations common in visual and bioinformatic data), but are assumed to be sparse.		 John Wright; Arvind Ganesh; Shankar Rao; Yigang Peng; Yi Ma;
2009 3 Fast Image Deconvolution Using Hyper-Laplacian Priors
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Highlight: In this paper we describe a deconvolution approach that is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors.		 Dilip Krishnan; Rob Fergus;
2009 4 Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise
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Highlight: In this paper we present a probabilistic model and use it to simultaneously infer the label of each image, the expertise of each labeler, and the difficulty of each image.		 Jacob Whitehill; Ting-fan Wu; Jacob Bergsma; Javier R. Movellan; Paul L. Ruvolo;
2009 5 Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks
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Highlight: In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks.		 Honglak Lee; Peter Pham; Yan Largman; Andrew Y. Ng;
2009 6 Zero-shot Learning with Semantic Output Codes
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Highlight: We consider the problem of zero-shot learning, where the goal is to learn a classifier $f: X \rightarrow Y$ that must predict novel values of $Y$ that were omitted from the training set.		 Mark Palatucci; Dean Pomerleau; Geoffrey E. Hinton; Tom M. Mitchell;
2009 7 Learning to Hash with Binary Reconstructive Embeddings
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Highlight: In this paper, we develop an algorithm for learning hash functions based on explicitly minimizing the reconstruction error between the original distances and the Hamming distances of the corresponding binary embeddings.		 Brian Kulis; Trevor Darrell;
2009 8 Nonlinear Learning Using Local Coordinate Coding
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Highlight: This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning.		 Kai Yu; Tong Zhang; Yihong Gong;
2009 9 Kernel Methods for Deep Learning
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Highlight: We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. On several problems, we obtain better results than previous, leading benchmarks from both SVMs with Gaussian kernels as well as deep belief nets.		 Youngmin Cho; Lawrence K. Saul;
2009 10 Rethinking LDA: Why Priors Matter
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Highlight: In this paper, we explore several classes of structured priors for topic models.		 Hanna M. Wallach; David M. Mimno; Andrew McCallum;
2009 11 Matrix Completion from Noisy Entries
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Highlight: Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries.		 Raghunandan Keshavan; Andrea Montanari; Sewoong Oh;
2009 12 Locality-sensitive Binary Codes from Shift-invariant Kernels
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Highlight: We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing.		 Maxim Raginsky; Svetlana Lazebnik;
2009 13 Replicated Softmax: An Undirected Topic Model
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Highlight: We describe efficient inference and learning procedures for such a family.		 Geoffrey E. Hinton; Russ R. Salakhutdinov;
2009 14 On The Convergence of The Concave-Convex Procedure
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Highlight: In this paper, we follow a different reasoning and show how Zangwills global convergence theory of iterative algorithms provides a natural framework to prove the convergence of CCCP, allowing a more elegant and simple proof. We also present an open problem on the issue of local convergence of CCCP.		 Gert R. Lanckriet; Bharath K. Sriperumbudur;
2009 15 Measuring Invariances in Deep Networks
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Highlight: In this paper, we propose a number of empirical tests that directly measure the degree to which these learned features are invariant to different image transforms.		 Ian Goodfellow; Honglak Lee; Quoc V. Le; Andrew Saxe; Andrew Y. Ng;
2008 1 Mixed Membership Stochastic Blockmodels
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Highlight: In this paper, we describe a class of latent variable models of such data called Mixed Membership Stochastic Blockmodels.		 Edo M. Airoldi; David M. Blei; Stephen E. Fienberg; Eric P. Xing;
2008 2 Near-optimal Regret Bounds for Reinforcement Learning
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Highlight: In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s1,s2 there is a policy which moves from s1 to s2 in at most D steps (on average).		 Peter Auer; Thomas Jaksch; Ronald Ortner;
2008 3 Supervised Dictionary Learning
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Highlight: This paper proposes a new step in that direction with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple decision functions.		 Julien Mairal; Jean Ponce; Guillermo Sapiro; Andrew Zisserman; Francis R. Bach;
2008 4 A Scalable Hierarchical Distributed Language Model
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Highlight: We introduce a fast hierarchical language model along with a simple feature-based algorithm for automatic construction of word trees from the data.		 Andriy Mnih; Geoffrey E. Hinton;
2008 5 Nonlinear Causal Discovery with Additive Noise Models
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Highlight: In this contribution we show that in fact the basic linear framework can be generalized to nonlinear models with additive noise.		 Patrik O. Hoyer; Dominik Janzing; Joris M. Mooij; Jonas Peters; Bernhard Sch�lkopf;
2008 6 Natural Image Denoising with Convolutional Networks
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Highlight: We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models.		 Viren Jain; Sebastian Seung;
2008 7 Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks
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Highlight: By combining two recent innovations in neural networks—multidimensional recurrent neural networks and connectionist temporal classification—this paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input.		 Alex Graves; J�rgen Schmidhuber;
2008 8 Weighted Sums of Random Kitchen Sinks: Replacing Minimization with Randomization in Learning
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Highlight: “I am training a randomly wired neural net to play tic-tac-toe,” Sussman replied.		 Ali Rahimi; Benjamin Recht;
2008 9 Policy Search for Motor Primitives in Robotics
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Highlight: In this paper, we extend previous work on policy learning from the immediate reward case to episodic reinforcement learning.		 Jens Kober; Jan R. Peters;
2008 10 Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity
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Highlight: We consider the problem of extracting smooth low-dimensional “neural trajectories” that summarize the activity recorded simultaneously from tens to hundreds of neurons on individual experimental trials.		 BYRON M. YU et. al.
2008 11 Privacy-preserving Logistic Regression
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Highlight: This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases.		 Kamalika Chaudhuri; Claire Monteleoni;
2008 12 Dynamic Visual Attention: Searching for Coding Length Increments
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Highlight: In this paper, a dynamic visual attention model based on the rarity of features is proposed.		 Xiaodi Hou; Liqing Zhang;
2008 13 Clustered Multi-Task Learning: A Convex Formulation
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Highlight: In this paper, we assume that tasks are clustered into groups, which are unknown beforehand, and that tasks within a group have similar weight vectors.		 Laurent Jacob; Jean-philippe Vert; Francis R. Bach;
2008 14 Domain Adaptation with Multiple Sources
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Highlight: This paper presents a theoretical analysis of the problem of adaptation with multiple sources.		 Yishay Mansour; Mehryar Mohri; Afshin Rostamizadeh;
2008 15 Sparse Online Learning Via Truncated Gradient
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Highlight: We propose a general method called truncated gradient to induce sparsity in the weights of online-learning algorithms with convex loss.		 John Langford; Lihong Li; Tong Zhang;
2007 1 Probabilistic Matrix Factorization
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 Andriy Mnih; Russ R. Salakhutdinov;
2007 2 Random Features for Large-Scale Kernel Machines
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 Ali Rahimi; Benjamin Recht;
2007 3 Supervised Topic Models
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Highlight: We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents.		 Jon D. Mcauliffe; David M. Blei;
2007 4 The Tradeoffs of Large Scale Learning
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Highlight: This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms.		 L�on Bottou; Olivier Bousquet;
2007 5 Multi-task Gaussian Process Prediction
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 Edwin V. Bonilla; Kian M. Chai; Christopher Williams;
2007 6 Sparse Deep Belief Net Model for Visual Area V2
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Highlight: This paper presents an unsupervised learning model that faithfully mimics certain properties of visual area V2.		 Honglak Lee; Chaitanya Ekanadham; Andrew Y. Ng;
2007 7 Sparse Feature Learning for Deep Belief Networks
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Highlight: Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the raw input.		 Marc’aurelio Ranzato; Y-lan Boureau; Yann L. Cun;
2007 8 Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
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Highlight: In this paper, we propose a direct importance estimation method that does not require the input density estimates.		 Masashi Sugiyama; Shinichi Nakajima; Hisashi Kashima; Paul V. Buenau; Motoaki Kawanabe;
2007 9 A Kernel Statistical Test of Independence
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 ARTHUR GRETTON et. al.
2007 10 Regret Minimization in Games with Incomplete Information
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 Martin Zinkevich; Michael Johanson; Michael Bowling; Carmelo Piccione;
2007 11 Multiple-Instance Active Learning
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Highlight: We present a framework for active learning in the multiple-instance setting.		 Burr Settles; Mark Craven; Soumya Ray;
2007 12 The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information
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Highlight: We present Epoch-Greedy, an algorithm for multi-armed bandits with observable side information.		 John Langford; Tong Zhang;
2007 13 Kernel Measures of Conditional Dependence
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 Kenji Fukumizu; Arthur Gretton; Xiaohai Sun; Bernhard Sch�lkopf;
2007 14 Predicting Human Gaze Using Low-level Saliency Combined with Face Detection
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Highlight: In this study we recorded eye-movements of human observers while they viewed photographs of natural scenes.		 Moran Cerf; Jonathan Harel; Wolfgang Einhaeuser; Christof Koch;
2007 15 McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
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 Ping Li; Qiang Wu; Christopher J. Burges;
2006 1 Greedy Layer-Wise Training of Deep Networks
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 Yoshua Bengio; Pascal Lamblin; Dan Popovici; Hugo Larochelle;
2006 2 Graph-Based Visual Saliency
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 Jonathan Harel; Christof Koch; Pietro Perona;
2006 3 Efficient Sparse Coding Algorithms
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 Honglak Lee; Alexis Battle; Rajat Raina; Andrew Y. Ng;
2006 4 A Kernel Method for The Two-Sample-Problem
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 Arthur Gretton; Karsten Borgwardt; Malte Rasch; Bernhard Sch�lkopf; Alex J. Smola;
2006 5 Analysis of Representations for Domain Adaptation
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 Shai Ben-David; John Blitzer; Koby Crammer; Fernando Pereira;
2006 6 Correcting Sample Selection Bias By Unlabeled Data
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 Jiayuan Huang; Arthur Gretton; Karsten Borgwardt; Bernhard Sch�lkopf; Alex J. Smola;
2006 7 Efficient Learning of Sparse Representations with An Energy-Based Model
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 Marc’aurelio Ranzato; Christopher Poultney; Sumit Chopra; Yann L. Cun;
2006 8 Map-Reduce for Machine Learning on Multicore
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 CHENG-TAO CHU et. al.
2006 9 Learning with Hypergraphs: Clustering, Classification, and Embedding
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 Dengyong Zhou; Jiayuan Huang; Bernhard Sch�lkopf;
2006 10 Learning to Rank with Nonsmooth Cost Functions
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 Christopher J. Burges; Robert Ragno; Quoc V. Le;
2006 11 Modeling Human Motion Using Binary Latent Variables
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 Graham W. Taylor; Geoffrey E. Hinton; Sam T. Roweis;
2006 12 Sparse Representation for Signal Classification
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 Ke Huang; Selin Aviyente;
2006 13 An Application of Reinforcement Learning to Aerobatic Helicopter Flight
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 Pieter Abbeel; Adam Coates; Morgan Quigley; Andrew Y. Ng;
2006 14 In-Network PCA and Anomaly Detection
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 LING HUANG et. al.
2006 15 Fast Discriminative Visual Codebooks Using Randomized Clustering Forests
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 Frank Moosmann; Bill Triggs; Frederic Jurie;
2005 1 Distance Metric Learning For Large Margin Nearest Neighbor Classification
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 Kilian Q. Weinberger; John Blitzer; Lawrence K. Saul;
2005 2 Laplacian Score For Feature Selection
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 Xiaofei He; Deng Cai; Partha Niyogi;
2005 3 Sparse Gaussian Processes Using Pseudo-inputs
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 Edward Snelson; Zoubin Ghahramani;
2005 4 Bayesian Surprise Attracts Human Attention
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 Laurent Itti; Pierre F. Baldi;
2005 5 Saliency Based On Information Maximization
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 Neil Bruce; John Tsotsos;
2005 6 Learning Depth From Single Monocular Images
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 Ashutosh Saxena; Sung H. Chung; Andrew Y. Ng;
2005 7 Infinite Latent Feature Models And The Indian Buffet Process
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 Zoubin Ghahramani; Thomas L. Griffiths;
2005 8 Multiple Instance Boosting For Object Detection
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 Cha Zhang; John C. Platt; Paul A. Viola;
2005 9 Correlated Topic Models
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 John D. Lafferty; David M. Blei;
2005 10 Metric Learning By Collapsing Classes
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 Amir Globerson; Sam T. Roweis;
2005 11 An Application Of Markov Random Fields To Range Sensing
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 James Diebel; Sebastian Thrun;
2005 12 Off-Road Obstacle Avoidance Through End-to-End Learning
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 Urs Muller; Jan Ben; Eric Cosatto; Beat Flepp; Yann L. Cun;
2005 13 Subsequence Kernels For Relation Extraction
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 Raymond J. Mooney; Razvan C. Bunescu;
2005 14 Gaussian Process Dynamical Models
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 Jack Wang; Aaron Hertzmann; David J. Fleet;
2005 15 Generalized Nonnegative Matrix Approximations With Bregman Divergences
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 Suvrit Sra; Inderjit S. Dhillon;
2004 1 Self-Tuning Spectral Clustering
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 Lihi Zelnik-manor; Pietro Perona;
2004 2 Semi-supervised Learning By Entropy Minimization
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 Yves Grandvalet; Yoshua Bengio;
2004 3 Neighbourhood Components Analysis
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 Jacob Goldberger; Geoffrey E. Hinton; Sam T. Roweis; Russ R. Salakhutdinov;
2004 4 Maximum-Margin Matrix Factorization
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 Nathan Srebro; Jason Rennie; Tommi S. Jaakkola;
2004 5 A Direct Formulation For Sparse PCA Using Semidefinite Programming
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 Alexandre D’aspremont; Laurent E. Ghaoui; Michael I. Jordan; Gert R. Lanckriet;
2004 6 Learning Syntactic Patterns For Automatic Hypernym Discovery
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 Rion Snow; Daniel Jurafsky; Andrew Y. Ng;
2004 7 Maximum Likelihood Estimation Of Intrinsic Dimension
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 Elizaveta Levina; Peter J. Bickel;
2004 8 Intrinsically Motivated Reinforcement Learning
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 Nuttapong Chentanez; Andrew G. Barto; Satinder P. Singh;
2004 9 Semi-Markov Conditional Random Fields For Information Extraction
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 Sunita Sarawagi; William W. Cohen;
2004 10 Two-Dimensional Linear Discriminant Analysis
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 Jieping Ye; Ravi Janardan; Qi Li;
2004 11 The Entire Regularization Path For The Support Vector Machine
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 Saharon Rosset; Robert Tibshirani; Ji Zhu; Trevor J. Hastie;
2004 12 Result Analysis Of The NIPS 2003 Feature Selection Challenge
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 Isabelle Guyon; Steve Gunn; Asa Ben-Hur; Gideon Dror;
2004 13 Integrating Topics And Syntax
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 Thomas L. Griffiths; Mark Steyvers; David M. Blei; Joshua B. Tenenbaum;
2004 14 An Investigation Of Practical Approximate Nearest Neighbor Algorithms
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 Ting Liu; Andrew W. Moore; Ke Yang; Alexander G. Gray;
2004 15 Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes
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 Yee W. Teh; Michael I. Jordan; Matthew J. Beal; David M. Blei;
2003 1 Locality Preserving Projections
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 Xiaofei He; Partha Niyogi;
2003 2 Learning With Local And Global Consistency
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 Dengyong Zhou; Olivier Bousquet; Thomas N. Lal; Jason Weston; Bernhard Sch�lkopf;
2003 3 Probability Estimates For Multi-Class Classification By Pairwise Coupling
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 Ting-fan Wu; Chih-jen Lin; Ruby C. Weng;
2003 4 Online Passive-Aggressive Algorithms
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 Shai Shalev-shwartz; Koby Crammer; Ofer Dekel; Yoram Singer;
2003 5 Max-Margin Markov Networks
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 Ben Taskar; Carlos Guestrin; Daphne Koller;
2003 6 Hierarchical Topic Models And The Nested Chinese Restaurant Process
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 Thomas L. Griffiths; Michael I. Jordan; Joshua B. Tenenbaum; David M. Blei;
2003 7 Out-of-Sample Extensions For LLE, Isomap, MDS, Eigenmaps, And Spectral Clustering
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 YOSHUA BENGIO et. al.
2003 8 Learning The K In K-means
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 Greg Hamerly; Charles Elkan;
2003 9 1-norm Support Vector Machines
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 Ji Zhu; Saharon Rosset; Robert Tibshirani; Trevor J. Hastie;
2003 10 Efficient And Robust Feature Extraction By Maximum Margin Criterion
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 Haifeng Li; Tao Jiang; Keshu Zhang;
2003 11 No Unbiased Estimator Of The Variance Of K-Fold Cross-Validation
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 Yoshua Bengio; Yves Grandvalet;
2003 12 When Does Non-Negative Matrix Factorization Give A Correct Decomposition Into Parts?
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 David Donoho; Victoria Stodden;
2003 13 Gaussian Process Latent Variable Models For Visualisation Of High Dimensional Data
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 Neil D. Lawrence;
2003 14 A Model For Learning The Semantics Of Pictures
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 Victor Lavrenko; R. Manmatha; Jiwoon Jeon;
2003 15 Ranking On Data Manifolds
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 Dengyong Zhou; Jason Weston; Arthur Gretton; Olivier Bousquet; Bernhard Sch�lkopf;
2002 1 Distance Metric Learning With Application To Clustering With Side-Information
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 Eric P. Xing; Michael I. Jordan; Stuart J. Russell; Andrew Y. Ng;
2002 2 Stochastic Neighbor Embedding
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 Geoffrey E. Hinton; Sam T. Roweis;
2002 3 Support Vector Machines For Multiple-Instance Learning
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 Stuart Andrews; Ioannis Tsochantaridis; Thomas Hofmann;
2002 4 Global Versus Local Methods In Nonlinear Dimensionality Reduction
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 Vin D. Silva; Joshua B. Tenenbaum;
2002 5 Fast Exact Inference With A Factored Model For Natural Language Parsing
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 Dan Klein; Christopher D. Manning;
2002 6 Learning Attractor Landscapes For Learning Motor Primitives
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 Auke J. Ijspeert; Jun Nakanishi; Stefan Schaal;
2002 7 An Impossibility Theorem For Clustering
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 Jon M. Kleinberg;
2002 8 Adaptive Nonlinear System Identification With Echo State Networks
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 Herbert Jaeger;
2002 9 Fast Sparse Gaussian Process Methods: The Informative Vector Machine
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 Ralf Herbrich; Neil D. Lawrence; Matthias Seeger;
2002 10 Recovering Intrinsic Images From A Single Image
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 Marshall F. Tappen; William T. Freeman; Edward H. Adelson;
2002 11 Cluster Kernels For Semi-Supervised Learning
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 Olivier Chapelle; Jason Weston; Bernhard Sch�lkopf;
2002 12 Charting A Manifold
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 Matthew Brand;
2002 13 Ranking With Large Margin Principle: Two Approaches
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 Amnon Shashua; Anat Levin;
2002 14 Parametric Mixture Models For Multi-Labeled Text
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 Naonori Ueda; Kazumi Saito;
2002 15 Identity Uncertainty And Citation Matching
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 Hanna Pasula; Bhaskara Marthi; Brian Milch; Stuart J. Russell; Ilya Shpitser;
2001 1 Latent Dirichlet Allocation
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 David M. Blei; Andrew Y. Ng; Michael I. Jordan;
2001 2 On Spectral Clustering: Analysis And An Algorithm
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 Andrew Y. Ng; Michael I. Jordan; Yair Weiss;
2001 3 Laplacian Eigenmaps And Spectral Techniques For Embedding And Clustering
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 Mikhail Belkin; Partha Niyogi;
2001 4 On Discriminative Vs. Generative Classifiers: A Comparison Of Logistic Regression And Naive Bayes
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 Andrew Y. Ng; Michael I. Jordan;
2001 5 A Kernel Method For Multi-labelled Classification
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 Andr� Elisseeff; Jason Weston;
2001 6 On Kernel-Target Alignment
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 Nello Cristianini; John Shawe-Taylor; Andr� Elisseeff; Jaz S. Kandola;
2001 7 A Natural Policy Gradient
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 Sham M. Kakade;
2001 8 Online Learning With Kernels
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 Jyrki Kivinen; Alex J. Smola; Robert C. Williamson;
2001 9 Convolution Kernels For Natural Language
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 Michael Collins; Nigel Duffy;
2001 10 EM-DD: An Improved Multiple-Instance Learning Technique
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 Qi Zhang; Sally A. Goldman;
2001 11 Spectral Relaxation For K-means Clustering
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 Hongyuan Zha; Xiaofeng He; Chris Ding; Ming Gu; Horst D. Simon;
2001 12 Partially Labeled Classification With Markov Random Walks
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 Martin Szummer; Tommi Jaakkola;
2001 13 Pranking With Ranking
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 Koby Crammer; Yoram Singer;
2001 14 Fast And Robust Classification Using Asymmetric AdaBoost And A Detector Cascade
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 Paul Viola; Michael Jones;
2001 15 Kernel Logistic Regression And The Import Vector Machine
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 Ji Zhu; Trevor Hastie;
2000 1 Algorithms For Non-negative Matrix Factorization
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Highlight: Two different multi- plicative algorithms for NMF are analyzed.		 Daniel D. Lee; H. Sebastian Seung;
2000 2 A Neural Probabilistic Language Model
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 Yoshua Bengio; R�jean Ducharme; Pascal Vincent;
2000 3 The Unscented Particle Filter
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 Rudolph van der Merwe; Arnaud Doucet; Nando de Freitas; Eric A. Wan;
2000 4 Incremental And Decremental Support Vector Machine Learning
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 Gert Cauwenberghs; Tomaso Poggio;
2000 5 Generalized Belief Propagation
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 Jonathan S. Yedidia; William T. Freeman; Yair Weiss;
2000 6 Feature Selection For SVMs
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 JASON WESTON et. al.
2000 7 Overfitting In Neural Nets: Backpropagation, Conjugate Gradient, And Early Stopping
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2000 8 The Kernel Trick For Distances
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2000 9 Shape Context: A New Descriptor For Shape Matching And Object Recognition
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2000 10 Automatic Choice Of Dimensionality For PCA
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2000 11 The Missing Link – A Probabilistic Model Of Document Content And Hypertext Connectivity
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2000 12 One Microphone Source Separation
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2000 13 Learning Segmentation By Random Walks
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2000 14 Sparse Greedy Gaussian Process Regression
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2000 15 Learning Switching Linear Models Of Human Motion
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1999 1 Policy Gradient Methods For Reinforcement Learning With Function Approximation
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1999 2 Actor-Critic Algorithms
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1999 3 Support Vector Method For Novelty Detection
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1999 4 Large Margin DAGs For Multiclass Classification
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 John C. Platt; Nello Cristianini; John Shawe-Taylor;
1999 5 The Infinite Gaussian Mixture Model
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1999 6 The Relevance Vector Machine
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1999 7 Inference For The Generalization Error
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1999 8 A Variational Baysian Framework For Graphical Models
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1999 9 Boosting Algorithms As Gradient Descent
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 Llew Mason; Jonathan Baxter; Peter L. Bartlett; Marcus R. Frean;
1999 10 Bayesian Map Learning In Dynamic Environments
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1999 11 Scale Mixtures Of Gaussians And The Statistics Of Natural Images
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1999 12 Variational Inference For Bayesian Mixtures Of Factor Analysers
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1999 13 Agglomerative Information Bottleneck
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1999 14 Model Selection For Support Vector Machines
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1999 15 Bayesian Network Induction Via Local Neighborhoods
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1998 1 Exploiting Generative Models In Discriminative Classifiers
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1998 2 Kernel PCA And De-Noising In Feature Spaces
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1998 3 Semi-Supervised Support Vector Machines
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1998 4 Support Vector Machines Applied To Face Recognition
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1998 5 SMEM Algorithm For Mixture Models
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1998 6 Using Analytic QP And Sparseness To Speed Training Of Support Vector Machines
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1998 7 Bayesian PCA
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1998 8 Gradient Descent For General Reinforcement Learning
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1998 9 Learning Nonlinear Dynamical Systems Using An EM Algorithm
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1998 10 Finite-Sample Convergence Rates For Q-Learning And Indirect Algorithms
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1998 11 Shrinking The Tube: A New Support Vector Regression Algorithm
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1998 12 Bayesian Modeling Of Human Concept Learning
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1998 13 Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees
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1998 14 Dynamically Adapting Kernels In Support Vector Machines
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1998 15 Replicator Equations, Maximal Cliques, And Graph Isomorphism
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1997 1 Classification By Pairwise Coupling
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1997 2 A Framework For Multiple-Instance Learning
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1997 3 EM Algorithms For PCA And SPCA
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1997 4 Reinforcement Learning With Hierarchies Of Machines
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1997 5 Intrusion Detection With Neural Networks
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1997 6 Extended ICA Removes Artifacts From Electroencephalographic Recordings
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1997 7 New Approximations Of Differential Entropy For Independent Component Analysis And Projection Pursuit
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1997 8 Refractoriness And Neural Precision
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1997 9 Prior Knowledge In Support Vector Kernels
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1997 10 Mapping A Manifold Of Perceptual Observations
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1997 11 Regression With Input-dependent Noise: A Gaussian Process Treatment
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1997 12 Learning To Order Things
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1997 13 Independent Component Analysis For Identification Of Artifacts In Magnetoencephalographic Recordings
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1997 14 Monotonic Networks
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1997 15 Dynamic Stochastic Synapses As Computational Units
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1996 1 Support Vector Regression Machines
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 Harris Drucker; Christopher J. C. Burges; Linda Kaufman; Alex J. Smola; Vladimir Vapnik;
1996 2 Support Vector Method For Function Approximation, Regression Estimation And Signal Processing
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1996 3 Analysis Of Temporal-Diffference Learning With Function Approximation
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1996 4 LSTM Can Solve Hard Long Time Lag Problems
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1996 5 Learning From Demonstration
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1996 6 MIMIC: Finding Optima By Estimating Probability Densities
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1996 7 Clustering Sequences With Hidden Markov Models
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1996 8 Probabilistic Interpretation Of Population Codes
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1996 9 Improving The Accuracy And Speed Of Support Vector Machines
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 Christopher J. C. Burges; Bernhard Sch�lkopf;
1996 10 Practical Confidence And Prediction Intervals
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1996 11 A Mixture Of Experts Classifier With Learning Based On Both Labelled And Unlabelled Data
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1996 12 Computing With Infinite Networks
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1996 13 Clustering Via Concave Minimization
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1996 14 For Valid Generalization The Size Of The Weights Is More Important Than The Size Of The Network
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1996 15 Reinforcement Learning For Dynamic Channel Allocation In Cellular Telephone Systems
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1995 1 A New Learning Algorithm For Blind Signal Separation
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1995 2 Independent Component Analysis Of Electroencephalographic Data
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1995 3 Generalization In Reinforcement Learning: Successful Examples Using Sparse Coarse Coding
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1995 4 Is Learning The N-th Thing Any Easier Than Learning The First?
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1995 5 Improving Elevator Performance Using Reinforcement Learning
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1995 6 Generalized Learning Vector Quantization
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1995 7 Human Face Detection In Visual Scenes
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1995 8 Factorial Hidden Markov Models
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1995 9 Generating Accurate And Diverse Members Of A Neural-Network Ensemble
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1995 10 Hierarchical Recurrent Neural Networks For Long-Term Dependencies
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1995 11 Exploiting Tractable Substructures In Intractable Networks
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1995 12 Extracting Tree-Structured Representations Of Trained Networks
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1995 13 Boosting Decision Trees
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1995 14 Gaussian Processes For Regression
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1995 15 Bayesian Methods For Mixtures Of Experts
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 Steve R. Waterhouse; David MacKay; Anthony J. Robinson;
1994 1 Active Learning With Statistical Models
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1994 2 Neural Network Ensembles, Cross Validation, And Active Learning
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 Anders Krogh; Jesper Vedelsby;
1994 3 A Growing Neural Gas Network Learns Topologies
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 Bernd Fritzke;
1994 4 A Solvable Connectionist Model Of Immediate Recall Of Ordered Lists
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 Neil Burgess;
1994 5 New Algorithms For 2D And 3D Point Matching: Pose Estimation And Correspondence
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 Steven Gold; Chien-Ping Lu; Anand Rangarajan; Suguna Pappu; Eric Mjolsness;
1994 6 Generalization In Reinforcement Learning: Safely Approximating The Value Function
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 Justin A. Boyan; Andrew W. Moore;
1994 7 Convergence Properties Of The K-Means Algorithms
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 L�on Bottou; Yoshua Bengio;
1994 8 Reinforcement Learning Algorithm For Partially Observable Markov Decision Problems
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 Tommi Jaakkola; Satinder P. Singh; Michael I. Jordan;
1994 9 Reinforcement Learning With Soft State Aggregation
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1994 10 Reinforcement Learning Methods For Continuous-Time Markov Decision Problems
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 Steven J. Bradtke; Michael O. Duff;
1994 11 Inferring Ground Truth From Subjective Labelling Of Venus Images
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1994 12 Learning Many Related Tasks At The Same Time With Backpropagation
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1994 13 An Alternative Model For Mixtures Of Experts
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1994 14 Finding Structure In Reinforcement Learning
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1994 15 Learning To Play The Game Of Chess
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 Sebastian Thrun;
1993 1 Autoencoders, Minimum Description Length And Helmholtz Free Energy
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 Geoffrey E. Hinton; Richard S. Zemel;
1993 2 Statistics Of Natural Images: Scaling In The Woods
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 Daniel L. Ruderman; William Bialek;
1993 3 Packet Routing In Dynamically Changing Networks: A Reinforcement Learning Approach
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1993 4 Supervised Learning From Incomplete Data Via An EM Approach
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1993 5 Neural Network Exploration Using Optimal Experiment Design
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1993 6 When Will A Genetic Algorithm Outperform Hill Climbing
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1993 7 Hoeffding Races: Accelerating Model Selection Search For Classification And Function Approximation
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1993 8 Learning Classification With Unlabeled Data
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1993 9 Bayesian Modeling And Classification Of Neural Signals
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 Michael S. Lewicki;
1993 10 The Parti-Game Algorithm For Variable Resolution Reinforcement Learning In Multidimensional State-Spaces
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1993 11 Non-Intrusive Gaze Tracking Using Artificial Neural Networks
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 Shumeet Baluja; Dean Pomerleau;
1993 12 Temporal Difference Learning Of Position Evaluation In The Game Of Go
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1993 13 Optimal Brain Surgeon: Extensions And Performance Comparisons
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1993 14 Surface Learning With Applications To Lipreading
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1993 15 Learning Curves: Asymptotic Values And Rate Of Convergence
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1992 1 Second Order Derivatives For Network Pruning: Optimal Brain Surgeon
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1992 2 Feudal Reinforcement Learning
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1992 3 Efficient Pattern Recognition Using A New Transformation Distance
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 Patrice Simard; Yann LeCun; John S. Denker;
1992 4 An Information-Theoretic Approach To Deciphering The Hippocampal Code
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 William E. Skaggs; Bruce L. McNaughton; Katalin M. Gothard;
1992 5 Discriminability-Based Transfer Between Neural Networks
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1992 6 Non-Linear Dimensionality Reduction
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1992 7 Word Space
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 Hinrich Sch�tze;
1992 8 Silicon Auditory Processors As Computer Peripherals
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1992 9 Bayesian Learning Via Stochastic Dynamics
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1992 10 Automatic Capacity Tuning Of Very Large VC-Dimension Classifiers
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 I. Guyon; B. Boser; V. Vapnik;
1992 11 Improving Performance In Neural Networks Using A Boosting Algorithm
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 Harris Drucker; Robert Schapire; Patrice Simard;
1992 12 A Fast Stochastic Error-Descent Algorithm For Supervised Learning And Optimization
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 Gert Cauwenberghs;
1992 13 Reinforcement Learning Applied To Linear Quadratic Regulation
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 Steven J. Bradtke;
1992 14 Explanation-Based Neural Network Learning For Robot Control
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 Tom M. Mitchell; Sebastian B. Thrun;
1992 15 Information, Prediction, And Query By Committee
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 Yoav Freund; H. Sebastian Seung; Eli Shamir; Naftali Tishby;
1991 1 A Simple Weight Decay Can Improve Generalization
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 Anders Krogh; John A. Hertz;
1991 2 Principles Of Risk Minimization For Learning Theory
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1991 3 Practical Issues In Temporal Difference Learning
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 Gerald Tesauro;
1991 4 The Effective Number Of Parameters: An Analysis Of Generalization And Regularization In Nonlinear Learning Systems
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 John E. Moody;
1991 5 Unsupervised Learning Of Distributions On Binary Vectors Using Two Layer Networks
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 Yoav Freund; David Haussler;
1991 6 Tangent Prop – A Formalism For Specifying Selected Invariances In An Adaptive Network
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 Patrice Simard; Bernard Victorri; Yann LeCun; John Denker;
1991 7 A Contrast Sensitive Silicon Retina With Reciprocal Synapses
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 Kwabena A. Boahen; Andreas G. Andreou;
1991 8 Improving The Performance Of Radial Basis Function Networks By Learning Center Locations
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 Dietrich Wettschereck; Thomas Dietterich;
1991 9 Multi-Digit Recognition Using A Space Displacement Neural Network
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 Ofer Matan; Christopher J. C. Burges; Yann LeCun; John S. Denker;
1991 10 Hierarchies Of Adaptive Experts
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 Michael I. Jordan; Robert A. Jacobs;
1991 11 Induction Of Multiscale Temporal Structure
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 Michael C. Mozer;
1991 12 Active Exploration In Dynamic Environments
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 Sebastian B. Thrun; Knut M�ller;
1991 13 Interpretation Of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks Into Rules
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 Geoffrey Towell; Jude W. Shavlik;
1991 14 Principled Architecture Selection For Neural Networks: Application To Corporate Bond Rating Prediction
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 John Moody; Joachim Utans;
1991 15 Towards Faster Stochastic Gradient Search
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 Christian Darken; John Moody;
1990 1 Generalization By Weight-Elimination With Application To Forecasting
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 Andreas S. Weigend; David E. Rumelhart; Bernardo A. Huberman;
1990 2 SEXNET: A Neural Network Identifies Sex From Human Faces
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 B.A. Golomb; D.T. Lawrence; T.J. Sejnowski;
1990 3 EMPATH: Face, Emotion, And Gender Recognition Using Holons
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 Garrison W. Cottrell; Janet Metcalfe;
1990 4 Transforming Neural-Net Output Levels To Probability Distributions
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 John S. Denker; Yann LeCun;
1990 5 Spoken Letter Recognition
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 Mark Fanty; Ronald Cole;
1990 6 The Recurrent Cascade-Correlation Architecture
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 Scott E. Fahlman;
1990 7 Note On Learning Rate Schedules For Stochastic Optimization
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 Christian Darken; John E. Moody;
1990 8 Integrated Segmentation And Recognition Of Hand-Printed Numerals
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 James D. Keeler; David E. Rumelhart; Wee Kheng Leow;
1990 9 A Competitive Modular Connectionist Architecture
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 Robert A. Jacobs; Michael I. Jordan;
1990 10 Reinforcement Learning In Markovian And Non-Markovian Environments
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 J�rgen Schmidhuber;
1990 11 Evaluation Of Adaptive Mixtures Of Competing Experts
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 Steven J. Nowlan; Geoffrey E. Hinton;
1990 12 Second Order Properties Of Error Surfaces: Learning Time And Generalization
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 Yann LeCun; Ido Kanter; Sara A. Solla;
1990 13 A Framework For The Cooperation Of Learning Algorithms
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 L�on Bottou; Patrick Gallinari;
1990 14 Bumptrees For Efficient Function, Constraint And Classification Learning
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 Stephen M. Omohundro;
1990 15 Constructing Hidden Units Using Examples And Queries
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 Eric B. Baum; Kevin J. Lang;
1989 1 Optimal Brain Damage
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 Yann LeCun; John S. Denker; Sara A. Solla;
1989 2 Handwritten Digit Recognition With A Back-Propagation Network
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 YANN LECUN et. al.
1989 3 The Cascade-Correlation Learning Architecture
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 Scott E. Fahlman; Christian Lebiere;
1989 4 Training Stochastic Model Recognition Algorithms As Networks Can Lead To Maximum Mutual Information Estimation Of Parameters
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 John S. Bridle;
1989 5 Training Connectionist Networks With Queries And Selective Sampling
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 Les E. Atlas; David A. Cohn; Richard E. Ladner;
1989 6 Maximum Likelihood Competitive Learning
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 Steven J. Nowlan;
1989 7 Higher Order Recurrent Networks And Grammatical Inference
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 C. Lee Giles; Guo-Zheng Sun; Hsing-Hen Chen; Yee-Chun Lee; Dong Chen;
1989 8 Synergy Of Clustering Multiple Back Propagation Networks
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 William P. Lincoln; Josef Skrzypek;
1989 9 Learning To Control An Unstable System With Forward Modeling
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 Michael I. Jordan; Robert A. Jacobs;
1989 10 Designing Application-Specific Neural Networks Using The Genetic Algorithm
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 Steven A. Harp; Tariq Samad; Aloke Guha;
1989 11 An Efficient Implementation Of The Back-propagation Algorithm On The Connection Machine CM-2
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 Xiru Zhang; Michael McKenna; Jill P. Mesirov; David L. Waltz;
1989 12 Algorithms For Better Representation And Faster Learning In Radial Basis Function Networks
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 Avijit Saha; James D. Keeler;
1989 13 Using Local Models To Control Movement
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 Christopher G. Atkeson;
1989 14 Meiosis Networks
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 Stephen Jose Hanson;
1989 15 Sequential Decision Problems And Neural Networks
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 A. G. Barto; R. S. Sutton; C. J. C. H. Watkins;
1988 1 ALVINN: An Autonomous Land Vehicle In A Neural Network
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 Dean A. Pomerleau;
1988 2 What Size Net Gives Valid Generalization?
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 Eric B. Baum; David Haussler;
1988 3 Training A 3-Node Neural Network Is NP-Complete
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 Avrim Blum; Ronald L. Rivest;
1988 4 Skeletonization: A Technique For Trimming The Fat From A Network Via Relevance Assessment
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 Michael C. Mozer; Paul Smolensky;
1988 5 Comparing Biases For Minimal Network Construction With Back-Propagation
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 Stephen Jose Hanson; Lorien Y. Pratt;
1988 6 Winner-Take-All Networks Of O(N) Complexity
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 J. Lazzaro; S. Ryckebusch; M.A. Mahowald; C. A. Mead;
1988 7 Links Between Markov Models And Multilayer Perceptrons
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 Herv� Bourlard; C. J. Wellekens;
1988 8 A Back-Propagation Algorithm With Optimal Use Of Hidden Units
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 Yves Chauvin;
1988 9 An Application Of The Principle Of Maximum Information Preservation To Linear Systems
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 Ralph Linsker;
1988 10 Fast Learning In Multi-Resolution Hierarchies
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 John Moody;
1988 11 Consonant Recognition By Modular Construction Of Large Phonemic Time-Delay Neural Networks
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 Alex Waibel;
1988 12 Neural Network Recognizer For Hand-Written Zip Code Digits
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 JOHN S. DENKER et. al.
1988 13 Efficient Parallel Learning Algorithms For Neural Networks
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 Alan H. Kramer; Alberto Sangiovanni-Vincentelli;
1988 14 On The K-Winners-Take-All Network
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 E. Majani; Ruth Erlanson; Yaser S. Abu-Mostafa;
1988 15 An Optimality Principle For Unsupervised Learning
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 Terence D. Sanger;
1987 1 How Neural Nets Work
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 Alan S. Lapedes; Robert M. Farber;
1987 2 Supervised Learning Of Probability Distributions By Neural Networks
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 Eric B. Baum; Frank Wilczek;
1987 3 Neural Net And Traditional Classifiers
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 William Y. Huang; Richard P. Lippmann;
1987 4 Learning Representations By Recirculation
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 Geoffrey E. Hinton; James L. McClelland;
1987 5 Generalization Of Back Propagation To Recurrent And Higher Order Neural Networks
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 Fernando J. Pineda;
1987 6 Constrained Differential Optimization
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 John C. Platt; Alan H. Barr;
1987 7 Static And Dynamic Error Propagation Networks With Application To Speech Coding
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 A. J. Robinson; F. Fallside;
1987 8 On The Power Of Neural Networks For Solving Hard Problems
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 Jehoshua Bruck; Joseph W. Goodman;
1987 9 An Optimization Network For Matrix Inversion
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 Ju-Seog Jang; Soo-Young Lee; Sang-Yung Shin;
1987 10 Encoding Geometric Invariances In Higher-Order Neural Networks
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 C. Lee Giles; R. D. Griffin; T. Maxwell;
1987 11 MURPHY: A Robot That Learns By Doing
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 Bartlett W. Mel;
1987 12 An Artificial Neural Network For Spatio-Temporal Bipolar Patterns: Application To Phoneme Classification
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 Les E. Atlas; Toshiteru Homma; Robert J. Marks II;
1987 13 Phasor Neural Networks
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 Andr� J. Noest;
1987 14 Stochastic Learning Networks And Their Electronic Implementation
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 Joshua Alspector; Robert B. Allen; Victor Hu; Srinagesh Satyanarayana;
1987 15 A Dynamical Approach To Temporal Pattern Processing
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 W. Scott Stornetta; Tad Hogg; Bernardo A. Huberman;