Most Influential NIPS Papers (2026-03 Version)

March 27, 2026March 29, 2026 admin

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: 2026-03)

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TABLE 1: Most Influential NIPS Papers (2026-03 Version)

Year	Rank	Paper	Author(s)
2025	1	DAPO: An Open-Source LLM Reinforcement Learning System at Scale IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose the Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO) algorithm, and fully open-source a state-of-the-art large-scale RL system that achieves 50 points on AIME 2024 using Qwen2.5-32B base model.	QIYING YU et. al.
2025	2	YOLOv12: Attention-Centric Real-Time Object Detectors IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes an attention-centric YOLO framework, namely YOLOv12, that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms.	Yunjie Tian; Qixiang Ye; David Doermann;
2025	3	Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond The Base Model? IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we take a critical look at \textit{the current state of RLVR} by systematically probing the reasoning capability boundaries of RLVR-trained LLMs across diverse model families, RL algorithms, and math/coding/visual reasoning benchmarks, using pass@\textit{k} at large \textit{k} values as the evaluation metric.	YANG YUE et. al.
2025	4	Large Language Diffusion Models IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The capabilities of large language models (LLMs) are widely regarded as relying on autoregressive models (ARMs). We challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm.	SHEN NIE et. al.
2025	5	Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on The Base Model IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training on the base model focusing on scalability, simplicity and accessibility.Embracing the principles of open-source, we release our source code, parameter settings, training data, and model weights across various sizes, fostering reproducibility and encouraging further exploration of the properties of related models.	JINGCHENG HU et. al.
2025	6	Beyond The 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we undertake a pioneering exploration of RLVR through the novel perspective of token entropy patterns, comprehensively analyzing how different tokens influence reasoning performance.	SHENZHI WANG et. al.
2025	7	The Illusion of Thinking: Understanding The Strengths and Limitations of Reasoning Models Via The Lens of Problem Complexity IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of compositional complexity while maintaining consistent logical structures.	PARSHIN SHOJAEE et. al.
2025	8	Video-R1: Reinforcing Video Reasoning in MLLMs IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by DeepSeek-R1’s success in eliciting reasoning abilities through rule-based reinforcement learning (RL), we introduce Video-R1 as the first attempt to systematically explore the R1 paradigm for incentivizing video reasoning within multimodal large language models (MLLMs).We have constructed two datasets: Video-R1-CoT-165k for SFT cold start and Video-R1-260k for RL training, both comprising image and video data.	KAITUO FENG et. al.
2025	9	A-Mem: Agentic Memory for LLM Agents IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, these systems’ fixed operations and structures limit their adaptability across diverse tasks. To address this limitation, this paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way.	WUJIANG XU et. al.
2025	10	Flow-GRPO: Training Flow Matching Models Via Online RL IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Flow-GRPO, the first method to integrate online policy gradient reinforcement learning (RL) into flow matching models.	JIE LIU et. al.
2025	11	WebThinker: Empowering Large Reasoning Models with Deep Research Capability IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, their reliance on static internal knowledge limits their performance on complex, knowledge-intensive tasks and hinders their ability to produce comprehensive research reports requiring synthesis of diverse web information. To address this, we propose WebThinker, a deep research agent that empowers LRMs to autonomously search the web, navigate among web pages, and draft reports during the reasoning process.	XIAOXI LI et. al.
2025	12	Reinforcement Learning for Reasoning in Large Language Models with One Training Example IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We show that reinforcement learning with verifiable reward using one training example (1-shot RLVR) is effective in incentivizing the math reasoning capabilities of large language models (LLMs).	YIPING WANG et. al.
2025	13	VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we aim to enhance the slow-thinking capabilities of vision-language models using reinforcement learning (without relying on distillation) to advance the state of the art.	HAOZHE WANG et. al.
2025	14	Mean Flows for One-step Generative Modeling IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a principled and effective framework for one-step generative modeling.	Zhengyang Geng; Mingyang Deng; Xingjian Bai; J Zico Kolter; Kaiming He;
2025	15	ToolRL: Reward Is All Tool Learning Needs IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present the first comprehensive study on reward design for tool selection and application tasks within the RL paradigm.	CHENG QIAN et. al.
2024	1	YOLOv10: Real-Time End-to-End Object Detection IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture.	AO WANG et. al.
2024	2	VMamba: Visual State Space Model IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we transplant Mamba, a state-space language model, into VMamba, a vision backbone that works in linear time complexity.	LIU YUE et. al.
2024	3	MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces MMLU-Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options.	YUBO WANG et. al.
2024	4	Depth Anything V2 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model.	LIHE YANG et. al.
2024	5	SimPO: Simple Preference Optimization with A Reference-Free Reward IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose SimPO, a simpler yet more effective approach.	Yu Meng; Mengzhou Xia; Danqi Chen;
2024	6	Visual Autoregressive Modeling: Scalable Image Generation Via Next-Scale Prediction IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine next-scale prediction or next-resolution prediction, diverging from the standard raster-scan next-token prediction.	Keyu Tian; Yi Jiang; Zehuan Yuan; BINGYUE PENG; Liwei Wang;
2024	7	CogVLM: Visual Expert for Pretrained Language Models IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CogVLM, a powerful open-source visual language foundation model.	WEIHAN WANG et. al.
2024	8	SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate how the role of interface design affects the performance of language model agents. As a result of this exploration, we introduce SWE-agent: a system that facilitates language model agents to autonomously use computers to solve software engineering tasks.	JOHN YANG et. al.
2024	9	Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach.	SHENGBANG TONG et. al.
2024	10	The FineWeb Datasets: Decanting The Web for The Finest Text Data at Scale IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce FineWeb, a 15-trillion token dataset derived from 96 Common Crawl snapshots that produces better-performing LLMs than other open pretraining datasets.	GUILHERME PENEDO et. al.
2024	11	Are We on The Right Way for Evaluating Large Vision-Language Models? IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we present MMStar, an elite vision-indispensable multi-modal benchmark comprising 1,500 samples meticulously selected by humans.	LIN CHEN et. al.
2024	12	SGLang: Efficient Execution of Structured Language Model Programs IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SGLang, a system for efficient execution of complex language model programs.	LIANMIN ZHENG et. al.
2024	13	Autoregressive Image Generation Without Vector Quantization IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose to model the per-token probability distribution using a diffusion procedure, which allows us to apply autoregressive models in a continuous-valued space.	Tianhong Li; Yonglong Tian; He Li; Mingyang Deng; Kaiming He;
2024	14	Tree of Attacks: Jailbreaking Black-Box LLMs Automatically IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present Tree of Attacks with Pruning* (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM.*	ANAY MEHROTRA et. al.
2024	15	Refusal in Language Models Is Mediated By A Single Direction IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we show that refusal is mediated by a one-dimensional subspace, across 13 popular open-source chat models up to 72B parameters in size.	ANDY ARDITI et. al.
2023	1	Visual Instruction Tuning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Direct Preference Optimization: Your Language Model Is Secretly A Reward Model IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	3	QLoRA: Efficient Finetuning of Quantized LLMs IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Tree of Thoughts: Deliberate Problem Solving with Large Language Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Toolformer: Language Models Can Teach Themselves to Use Tools IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	7	Self-Refine: Iterative Refinement with Self-Feedback IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Reflexion: Language Agents with Verbal Reinforcement Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	Jailbroken: How Does LLM Safety Training Fail? IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Is Your Code Generated By ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such limitation in the existing benchmarks begs the following question: In the era of LLMs, is the code generated really correct? To answer this, we propose EvalPlus – a code synthesis benchmarking framework to rigorously evaluate the functional correctness of LLM-synthesized code.	Jiawei Liu; Chunqiu Steven Xia; Yuyao Wang; LINGMING ZHANG;
2023	11	LLaVA-Med: Training A Large Language-and-Vision Assistant for Biomedicine in One Day IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.	CHUNYUAN LI et. al.
2023	12	HuggingGPT: Solving AI Tasks with ChatGPT and Its Friends in Hugging Face IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	13	ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	14	LIMA: Less Is More for Alignment IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	15	Inference-Time Intervention: Eliciting Truthful Answers from A Language Model IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Inference-Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs).	Kenneth Li; Oam Patel; Fernanda Viégas; Hanspeter Pfister; Martin Wattenberg;
2022	1	Training Language Models to Follow Instructions with Human Feedback IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	Elucidating The Design Space of Diffusion-Based Generative Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	Video Diffusion Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Locating and Editing Factual Associations in GPT IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose an exact formulation of the solution of diffusion ODEs.	CHENG LU et. al.
2022	12	Learn to Explain: Multimodal Reasoning Via Thought Chains for Science Question Answering IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we present Science Question Answering (SQA), a new benchmark that consists of ~21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations.	PAN LU et. al.
2022	13	The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	14	VideoMAE: Masked Autoencoders Are Data-Efficient Learners for Self-Supervised Video Pre-Training IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	15	Solving Quantitative Reasoning Problems with Language Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper studies the long-term forecasting problem of time series.	haixu wu; Jiehui Xu; Jianmin Wang; Mingsheng Long;
2021	4	MLP-Mixer: An All-MLP Architecture for Vision IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	6	NeuS: Learning Neural Implicit Surfaces By Volume Rendering for Multi-view Reconstruction IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	7	Transformer in Transformer IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	Decision Transformer: Reinforcement Learning Via Sequence Modeling IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem.	LILI CHEN et. al.
2021	9	Alias-Free Generative Adversarial Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Per-Pixel Classification Is Not All You Need for Semantic Segmentation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	CoAtNet: Marrying Convolution and Attention for All Data Sizes IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Structured Denoising Diffusion Models in Discrete State-Spaces IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. [2021], by going beyond corruption processes with uniform transition probabilities.	Jacob Austin; Daniel Johnson; Rianne van den Berg; Jonathan Ho; Daniel Tarlow;
2021	13	Volume Rendering of Neural Implicit Surfaces IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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? IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Twins: Revisiting The Design of Spatial Attention in Vision Transformers IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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.
2020	1	Language Models Are Few-Shot Learners IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	4	Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	Supervised Contrastive Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods.	KIHYUK SOHN et. al.
2020	8	RandAugment: Practical Automated Data Augmentation with A Reduced Search Space IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we rethink the process of designing automated data augmentation strategies.	Ekin Dogus Cubuk; Barret Zoph; Jon Shlens; Quoc Le;
2020	9	Open Graph Benchmark: Datasets for Machine Learning on Graphs IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Implicit Neural Representations with Periodic Activation Functions IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	12	Big Bird: Transformers for Longer Sequences IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.	MANZIL ZAHEER et. al.
2020	13	Unsupervised Data Augmentation for Consistency Training IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	14	Graph Contrastive Learning with Augmentations IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data.	YUNING YOU et. al.
2020	15	HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis.	Jungil Kong; Jaehyeon Kim; Jaekyoung Bae;
2019	1	PyTorch: An Imperative Style, High-Performance Deep Learning Library IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Generative Modeling By Estimating Gradients of The Data Distribution IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	4	ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	MixMatch: A Holistic Approach to Semi-Supervised Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	6	Cross-lingual Language Model Pretraining IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining.	Alexis CONNEAU; Guillaume Lample;
2019	7	Deep Leakage from Gradients IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	When Does Label Smoothing Help? IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Generating Diverse High-Fidelity Images with VQ-VAE-2 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	Adversarial Examples Are Not Bugs, They Are Features IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	12	GPipe: Efficient Training of Giant Neural Networks Using Pipeline Parallelism IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	13	Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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;
2019	14	Enhancing The Locality and Breaking The Memory Bottleneck of Transformer on Time Series Forecasting IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose to tackle such forecasting problem with Transformer.	SHIYANG LI et. al.
2019	15	Modeling Tabular Data Using Conditional GAN IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We design CTGAN, which uses a conditional generative adversarial network to address these challenges.	Lei Xu; Maria Skoularidou; Alfredo Cuesta-Infante; Kalyan Veeramachaneni;
2018	1	Neural Ordinary Differential Equations IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a new family of deep neural network models.	Tian Qi Chen; Yulia Rubanova; Jesse Bettencourt; David K. Duvenaud;
2018	2	Neural Tangent Kernel: Convergence and Generalization in Neural Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	3	Glow: Generative Flow with Invertible 1×1 Convolutions IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper we propose Glow, a simple type of generative flow using invertible 1×1 convolution.	Durk P. Kingma; Prafulla Dhariwal;
2018	4	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	PointCNN: Convolution On X-Transformed Points IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a simple and general framework for feature learning from point cloud.	YANGYAN LI et. al.
2018	6	Conditional Adversarial Domain Adaptation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	Hierarchical Graph Representation Learning with Differentiable Pooling IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Sanity Checks for Saliency Maps IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	Link Prediction Based on Graph Neural Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Multi-Task Learning As Multi-Objective Optimization IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we explicitly cast multi-task learning as multi-objective optimization, with the overall objective of finding a Pareto optimal solution.	Ozan Sener; Vladlen Koltun;
2018	14	Joint Autoregressive and Hierarchical Priors for Learned Image Compression IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization.	David Minnen; Johannes Ball�; George D. Toderici;
2018	15	TADAM: Task Dependent Adaptive Metric for Improved Few-shot Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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;
2017	1	Attention Is All You Need IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	PointNet++: Deep Hierarchical Feature Learning on Point Sets in A Metric Space IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	6	LightGBM: A Highly Efficient Gradient Boosting Decision Tree IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	7	Improved Training of Wasserstein GANs IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	Prototypical Networks for Few-shot Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Neural Discrete Representation Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We explore deep reinforcement learning methods for multi-agent domains.	RYAN LOWE et. al.
2017	13	Mean Teachers Are Better Role Models: Weight-averaged Consistency Targets Improve Semi-supervised Deep Learning Results IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions.	Antti Tarvainen; Harri Valpola;
2017	14	Dynamic Routing Between Capsules IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	15	Deep Reinforcement Learning from Human Preferences IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments.	PAUL F. CHRISTIANO et. al.
2016	1	Improved Techniques for Training GANs IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present region-based, fully convolutional networks for accurate and efficient object detection.	Jifeng Dai; Yi Li; Kaiming He; Jian Sun;
2016	5	Equality of Opportunity in Supervised Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	6	InfoGAN: Interpretable Representation Learning By Information Maximizing Generative Adversarial Nets IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	7	Man Is to Computer Programmer As Woman Is to Homemaker? Debiasing Word Embeddings IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Improved Deep Metric Learning with Multi-class N-pair Loss Objective IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	12	Learning to Learn By Gradient Descent By Gradient Descent IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	13	Binarized Neural Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	14	Learning A Probabilistic Latent Space of Object Shapes Via 3D Generative-Adversarial Modeling IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	15	Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	3	Spatial Transformer Networks IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	4	Learning Both Weights and Connections for Efficient Neural Network IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Learning Structured Output Representation Using Deep Conditional Generative Models IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	Convolutional Networks on Graphs for Learning Molecular Fingerprints IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	Pointer Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	BinaryConnect: Training Deep Neural Networks with Binary Weights During Propagations IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	Attention-Based Models for Speech Recognition IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Efficient and Robust Automated Machine Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters).	MATTHIAS FEURER et. al.
2014	1	Sequence to Sequence Learning with Neural Networks IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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? IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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? IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Identifying and Attacking The Saddle Point Problem in High-dimensional Non-convex Optimization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by these arguments, we propose a new approach to second-order optimization, the saddle-free Newton method, that can rapidly escape high dimensional saddle points, unlike gradient descent and quasi-Newton methods.	YANN N. DAUPHIN et. al.
2013	1	Distributed Representations of Words and Phrases and Their Compositionality IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Learning with Noisy Labels IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	11	Deep Content-based Music Recommendation IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	12	Understanding Variable Importances in Forests of Randomized Trees IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	13	Learning A Deep Compact Image Representation for Visual Tracking IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	14	Multi-Task Bayesian Optimization IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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;
2013	15	More Effective Distributed ML Via A Stale Synchronous Parallel Parameter Server IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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.
2012	1	ImageNet Classification with Deep Convolutional Neural Networks IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a Deep Boltzmann Machine for learning a generative model of multimodal data.	Nitish Srivastava; Russ R. Salakhutdinov;
2012	6	Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	7	Image Denoising and Inpainting with Deep Neural Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	Hamming Distance Metric Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Optimal Kernel Choice for Large-scale Two-sample Tests IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Convolutional-Recursive Deep Learning for 3D Object Classification IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Richard Socher; Brody Huval; Bharath Bath; Christopher D. Manning; Andrew Y. Ng;
2012	11	Communication-Efficient Algorithms for Statistical Optimization IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Generalizing from Several Related Classification Tasks to A New Unlabeled Sample IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present generalization error analysis, describe universal kernels, and establish universal consistency of the proposed methodology.	Gilles Blanchard; Gyemin Lee; Clayton Scott;
2011	15	Contextual Gaussian Process Bandit Optimization IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We model the payoff function as a sample from a Gaussian process defined over the joint context-action space, and develop CGP-UCB, an intuitive upper-confidence style algorithm.	Andreas Krause; Cheng S. Ong;
2010	1	Online Learning for Latent Dirichlet Allocation IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce an alternative way to approximate the maximum expected value for any set of random variables.	Hado V. Hasselt;
2010	3	Self-Paced Learning for Latent Variable Models IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	4	Parallelized Stochastic Gradient Descent IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	Learning To Count Objects in Images IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces a Monte-Carlo algorithm for online planning in large POMDPs.	David Silver; Joel Veness;
2010	7	Extended Bayesian Information Criteria for Gaussian Graphical Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	The Multidimensional Wisdom of Crowds IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Peter Welinder; Steve Branson; Pietro Perona; Serge J. Belongie;
2010	9	Robust PCA Via Outlier Pursuit IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Active Learning By Querying Informative and Representative Examples IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jonathan Chang; Sean Gerrish; Chong Wang; Jordan L. Boyd-graber; David M. Blei;
2009	2	Fast Image Deconvolution Using Hyper-Laplacian Priors IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	3	Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices Via Convex Optimization IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	4	Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Kernel Methods for Deep Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	Nonlinear Learning Using Local Coordinate Coding IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Rethinking LDA: Why Priors Matter IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Nonlinear Causal Discovery with Additive Noise Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	5	A Scalable Hierarchical Distributed Language Model IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	6	Natural Image Denoising with Convolutional Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	Policy Search for Motor Primitives in Robotics IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	8	Weighted Sums of Random Kitchen Sinks: Replacing Minimization with Randomization in Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: “I am training a randomly wired neural net to play tic-tac-toe,” Sussman replied.	Ali Rahimi; Benjamin Recht;
2008	9	Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, a dynamic visual attention model based on the rarity of features is proposed.	Xiaodi Hou; Liqing Zhang;
2008	13	Spectral Hashing IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard.	Yair Weiss; Antonio Torralba; Rob Fergus;
2008	14	Domain Adaptation with Multiple Sources IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a theoretical analysis of the problem of adaptation with multiple sources.	Yishay Mansour; Mehryar Mohri; Afshin Rostamizadeh;
2008	15	Clustered Multi-Task Learning: A Convex Formulation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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;
2007	1	Probabilistic Matrix Factorization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andriy Mnih; Russ R. Salakhutdinov;
2007	2	Random Features for Large-Scale Kernel Machines IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ali Rahimi; Benjamin Recht;
2007	3	Supervised Topic Models IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Edwin V. Bonilla; Kian M. Chai; Christopher Williams;
2007	6	Sparse Deep Belief Net Model for Visual Area V2 IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	A Kernel Statistical Test of Independence IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	ARTHUR GRETTON et. al.
2007	8	Sparse Feature Learning for Deep Belief Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	9	Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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	10	Regret Minimization in Games with Incomplete Information IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Martin Zinkevich; Michael Johanson; Michael Bowling; Carmelo Piccione;
2007	11	The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Epoch-Greedy, an algorithm for multi-armed bandits with observable side information.	John Langford; Tong Zhang;
2007	12	Multiple-Instance Active Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a framework for active learning in the multiple-instance setting.	Burr Settles; Mark Craven; Soumya Ray;
2007	13	Kernel Measures of Conditional Dependence IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Kenji Fukumizu; Arthur Gretton; Xiaohai Sun; Bernhard Sch�lkopf;
2007	14	McRank: Learning to Rank Using Multiple Classification and Gradient Boosting IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ping Li; Qiang Wu; Christopher J. Burges;
2007	15	Predicting Human Gaze Using Low-level Saliency Combined with Face Detection IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save 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;
2006	1	Greedy Layer-Wise Training of Deep Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yoshua Bengio; Pascal Lamblin; Dan Popovici; Hugo Larochelle;
2006	2	Graph-Based Visual Saliency IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jonathan Harel; Christof Koch; Pietro Perona;
2006	3	Efficient Sparse Coding Algorithms IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Honglak Lee; Alexis Battle; Rajat Raina; Andrew Y. Ng;
2006	4	A Kernel Method for The Two-Sample-Problem IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Arthur Gretton; Karsten Borgwardt; Malte Rasch; Bernhard Sch�lkopf; Alex J. Smola;
2006	5	Analysis of Representations for Domain Adaptation IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Shai Ben-David; John Blitzer; Koby Crammer; Fernando Pereira;
2006	6	Correcting Sample Selection Bias By Unlabeled Data IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jiayuan Huang; Arthur Gretton; Karsten Borgwardt; Bernhard Sch�lkopf; Alex J. Smola;
2006	7	Learning with Hypergraphs: Clustering, Classification, and Embedding IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Dengyong Zhou; Jiayuan Huang; Bernhard Sch�lkopf;
2006	8	Efficient Learning of Sparse Representations with An Energy-Based Model IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Marc’aurelio Ranzato; Christopher Poultney; Sumit Chopra; Yann L. Cun;
2006	9	Map-Reduce for Machine Learning on Multicore IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	CHENG-TAO CHU et. al.
2006	10	Learning to Rank with Nonsmooth Cost Functions IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christopher J. Burges; Robert Ragno; Quoc V. Le;
2006	11	Modeling Human Motion Using Binary Latent Variables IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Graham W. Taylor; Geoffrey E. Hinton; Sam T. Roweis;
2006	12	Sparse Representation for Signal Classification IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ke Huang; Selin Aviyente;
2006	13	An Application of Reinforcement Learning to Aerobatic Helicopter Flight IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Pieter Abbeel; Adam Coates; Morgan Quigley; Andrew Y. Ng;
2006	14	Fast Discriminative Visual Codebooks Using Randomized Clustering Forests IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Frank Moosmann; Bill Triggs; Frederic Jurie;
2006	15	In-Network PCA and Anomaly Detection IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	LING HUANG et. al.
2005	1	Distance Metric Learning For Large Margin Nearest Neighbor Classification IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Kilian Q. Weinberger; John Blitzer; Lawrence K. Saul;
2005	2	Laplacian Score For Feature Selection IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Xiaofei He; Deng Cai; Partha Niyogi;
2005	3	Sparse Gaussian Processes Using Pseudo-inputs IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Edward Snelson; Zoubin Ghahramani;
2005	4	Bayesian Surprise Attracts Human Attention IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Laurent Itti; Pierre F. Baldi;
2005	5	Saliency Based On Information Maximization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Neil Bruce; John Tsotsos;
2005	6	Learning Depth From Single Monocular Images IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ashutosh Saxena; Sung H. Chung; Andrew Y. Ng;
2005	7	Infinite Latent Feature Models And The Indian Buffet Process IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Zoubin Ghahramani; Thomas L. Griffiths;
2005	8	Multiple Instance Boosting For Object Detection IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Cha Zhang; John C. Platt; Paul A. Viola;
2005	9	Metric Learning By Collapsing Classes IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Amir Globerson; Sam T. Roweis;
2005	10	An Application Of Markov Random Fields To Range Sensing IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	James Diebel; Sebastian Thrun;
2005	11	Off-Road Obstacle Avoidance Through End-to-End Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Urs Muller; Jan Ben; Eric Cosatto; Beat Flepp; Yann L. Cun;
2005	12	Correlated Topic Models IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John D. Lafferty; David M. Blei;
2005	13	Gaussian Process Dynamical Models IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jack Wang; Aaron Hertzmann; David J. Fleet;
2005	14	Subsequence Kernels For Relation Extraction IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Raymond J. Mooney; Razvan C. Bunescu;
2005	15	Generalized Nonnegative Matrix Approximations With Bregman Divergences IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Suvrit Sra; Inderjit S. Dhillon;
2004	1	Semi-supervised Learning By Entropy Minimization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yves Grandvalet; Yoshua Bengio;
2004	2	Self-Tuning Spectral Clustering IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Lihi Zelnik-manor; Pietro Perona;
2004	3	Neighbourhood Components Analysis IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jacob Goldberger; Geoffrey E. Hinton; Sam T. Roweis; Russ R. Salakhutdinov;
2004	4	Maximum-Margin Matrix Factorization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Nathan Srebro; Jason Rennie; Tommi S. Jaakkola;
2004	5	A Direct Formulation For Sparse PCA Using Semidefinite Programming IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Alexandre D’aspremont; Laurent E. Ghaoui; Michael I. Jordan; Gert R. Lanckriet;
2004	6	Maximum Likelihood Estimation Of Intrinsic Dimension IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Elizaveta Levina; Peter J. Bickel;
2004	7	Intrinsically Motivated Reinforcement Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Nuttapong Chentanez; Andrew G. Barto; Satinder P. Singh;
2004	8	Learning Syntactic Patterns For Automatic Hypernym Discovery IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Rion Snow; Daniel Jurafsky; Andrew Y. Ng;
2004	9	Semi-Markov Conditional Random Fields For Information Extraction IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sunita Sarawagi; William W. Cohen;
2004	10	Two-Dimensional Linear Discriminant Analysis IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jieping Ye; Ravi Janardan; Qi Li;
2004	11	The Entire Regularization Path For The Support Vector Machine IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Saharon Rosset; Robert Tibshirani; Ji Zhu; Trevor J. Hastie;
2004	12	Result Analysis Of The NIPS 2003 Feature Selection Challenge IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Isabelle Guyon; Steve Gunn; Asa Ben-Hur; Gideon Dror;
2004	13	Integrating Topics And Syntax IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Thomas L. Griffiths; Mark Steyvers; David M. Blei; Joshua B. Tenenbaum;
2004	14	An Investigation Of Practical Approximate Nearest Neighbor Algorithms IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ting Liu; Andrew W. Moore; Ke Yang; Alexander G. Gray;
2004	15	Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yee W. Teh; Michael I. Jordan; Matthew J. Beal; David M. Blei;
2003	1	Locality Preserving Projections IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Xiaofei He; Partha Niyogi;
2003	2	Learning With Local And Global Consistency IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Dengyong Zhou; Olivier Bousquet; Thomas N. Lal; Jason Weston; Bernhard Sch�lkopf;
2003	3	Online Passive-Aggressive Algorithms IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Shai Shalev-shwartz; Koby Crammer; Ofer Dekel; Yoram Singer;
2003	4	Probability Estimates For Multi-Class Classification By Pairwise Coupling IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ting-fan Wu; Chih-jen Lin; Ruby C. Weng;
2003	5	No Unbiased Estimator Of The Variance Of K-Fold Cross-Validation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yoshua Bengio; Yves Grandvalet;
2003	6	Max-Margin Markov Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ben Taskar; Carlos Guestrin; Daphne Koller;
2003	7	Hierarchical Topic Models And The Nested Chinese Restaurant Process IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Thomas L. Griffiths; Michael I. Jordan; Joshua B. Tenenbaum; David M. Blei;
2003	8	Efficient And Robust Feature Extraction By Maximum Margin Criterion IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Haifeng Li; Tao Jiang; Keshu Zhang;
2003	9	Learning The K In K-means IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Greg Hamerly; Charles Elkan;
2003	10	Out-of-Sample Extensions For LLE, Isomap, MDS, Eigenmaps, And Spectral Clustering IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	YOSHUA BENGIO et. al.
2003	11	1-norm Support Vector Machines IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ji Zhu; Saharon Rosset; Robert Tibshirani; Trevor J. Hastie;
2003	12	When Does Non-Negative Matrix Factorization Give A Correct Decomposition Into Parts? IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David Donoho; Victoria Stodden;
2003	13	Gaussian Process Latent Variable Models For Visualisation Of High Dimensional Data IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Neil D. Lawrence;
2003	14	*ARA: Anytime A* With Provable Bounds On Sub-Optimality IF:8** Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Maxim Likhachev; Geoffrey J. Gordon; Sebastian Thrun;
2003	15	A Model For Learning The Semantics Of Pictures IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Victor Lavrenko; R. Manmatha; Jiwoon Jeon;
2002	1	Distance Metric Learning With Application To Clustering With Side-Information IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Eric P. Xing; Michael I. Jordan; Stuart J. Russell; Andrew Y. Ng;
2002	2	Stochastic Neighbor Embedding IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Geoffrey E. Hinton; Sam T. Roweis;
2002	3	Support Vector Machines For Multiple-Instance Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Stuart Andrews; Ioannis Tsochantaridis; Thomas Hofmann;
2002	4	Global Versus Local Methods In Nonlinear Dimensionality Reduction IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Vin D. Silva; Joshua B. Tenenbaum;
2002	5	Fast Exact Inference With A Factored Model For Natural Language Parsing IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Dan Klein; Christopher D. Manning;
2002	6	Learning Attractor Landscapes For Learning Motor Primitives IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Auke J. Ijspeert; Jun Nakanishi; Stefan Schaal;
2002	7	Adaptive Nonlinear System Identification With Echo State Networks IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Herbert Jaeger;
2002	8	An Impossibility Theorem For Clustering IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jon M. Kleinberg;
2002	9	Adaptation And Unsupervised Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Peter Dayan; Maneesh Sahani; Gregoire Deback;
2002	10	Fast Sparse Gaussian Process Methods: The Informative Vector Machine IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ralf Herbrich; Neil D. Lawrence; Matthias Seeger;
2002	11	Recovering Intrinsic Images From A Single Image IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Marshall F. Tappen; William T. Freeman; Edward H. Adelson;
2002	12	Cluster Kernels For Semi-Supervised Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Olivier Chapelle; Jason Weston; Bernhard Sch�lkopf;
2002	13	Charting A Manifold IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Matthew Brand;
2002	14	Ranking With Large Margin Principle: Two Approaches IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Amnon Shashua; Anat Levin;
2002	15	Learning With Multiple Labels IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Rong Jin; Zoubin Ghahramani;
2001	1	Latent Dirichlet Allocation IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David M. Blei; Andrew Y. Ng; Michael I. Jordan;
2001	2	On Spectral Clustering: Analysis And An Algorithm IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andrew Y. Ng; Michael I. Jordan; Yair Weiss;
2001	3	Laplacian Eigenmaps And Spectral Techniques For Embedding And Clustering IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Mikhail Belkin; Partha Niyogi;
2001	4	On Discriminative Vs. Generative Classifiers: A Comparison Of Logistic Regression And Naive Bayes IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andrew Y. Ng; Michael I. Jordan;
2001	5	A Kernel Method For Multi-labelled Classification IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andr� Elisseeff; Jason Weston;
2001	6	A Natural Policy Gradient IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sham M. Kakade;
2001	7	On Kernel-Target Alignment IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Nello Cristianini; John Shawe-Taylor; Andr� Elisseeff; Jaz S. Kandola;
2001	8	Online Learning With Kernels IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jyrki Kivinen; Alex J. Smola; Robert C. Williamson;
2001	9	Convolution Kernels For Natural Language IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael Collins; Nigel Duffy;
2001	10	EM-DD: An Improved Multiple-Instance Learning Technique IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Qi Zhang; Sally A. Goldman;
2001	11	Pranking With Ranking IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Koby Crammer; Yoram Singer;
2001	12	Spectral Relaxation For K-means Clustering IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Hongyuan Zha; Xiaofeng He; Chris Ding; Ming Gu; Horst D. Simon;
2001	13	Partially Labeled Classification With Markov Random Walks IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Martin Szummer; Tommi Jaakkola;
2001	14	The Infinite Hidden Markov Model IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Matthew J. Beal; Zoubin Ghahramani; Carl E. Rasmussen;
2001	15	Fast And Robust Classification Using Asymmetric AdaBoost And A Detector Cascade IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Paul Viola; Michael Jones;
2000	1	Algorithms For Non-negative Matrix Factorization IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Two different multi- plicative algorithms for NMF are analyzed.	Daniel D. Lee; H. Sebastian Seung;
2000	2	A Neural Probabilistic Language Model IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yoshua Bengio; R�jean Ducharme; Pascal Vincent;
2000	3	The Unscented Particle Filter IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Rudolph van der Merwe; Arnaud Doucet; Nando de Freitas; Eric A. Wan;
2000	4	Text Classification Using String Kernels IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Huma Lodhi; John Shawe-Taylor; Nello Cristianini; Christopher J. C. H. Watkins;
2000	5	Incremental And Decremental Support Vector Machine Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Gert Cauwenberghs; Tomaso Poggio;
2000	6	Generalized Belief Propagation IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jonathan S. Yedidia; William T. Freeman; Yair Weiss;
2000	7	Feature Selection For SVMs IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	JASON WESTON et. al.
2000	8	Overfitting In Neural Nets: Backpropagation, Conjugate Gradient, And Early Stopping IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Rich Caruana; Steve Lawrence; C. Lee Giles;
2000	9	The Kernel Trick For Distances IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Bernhard Sch�lkopf;
2000	10	Shape Context: A New Descriptor For Shape Matching And Object Recognition IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Serge Belongie; Jitendra Malik; Jan Puzicha;
2000	11	Automatic Choice Of Dimensionality For PCA IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Thomas P. Minka;
2000	12	The Missing Link – A Probabilistic Model Of Document Content And Hypertext Connectivity IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David A. Cohn; Thomas Hofmann;
2000	13	One Microphone Source Separation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sam T. Roweis;
2000	14	Incorporating Second-Order Functional Knowledge For Better Option Pricing IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Charles Dugas; Yoshua Bengio; Fran�ois B�lisle; Claude Nadeau; Ren� Garcia;
2000	15	Learning Segmentation By Random Walks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Marina Meila; Jianbo Shi;
1999	1	Policy Gradient Methods For Reinforcement Learning With Function Approximation IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Richard S. Sutton; David A. McAllester; Satinder P. Singh; Yishay Mansour;
1999	2	Actor-Critic Algorithms IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Vijay R. Konda; John N. Tsitsiklis;
1999	3	Support Vector Method For Novelty Detection IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Bernhard Sch�lkopf; Robert C. Williamson; Alex J. Smola; John Shawe-Taylor; John C. Platt;
1999	4	Large Margin DAGs For Multiclass Classification IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John C. Platt; Nello Cristianini; John Shawe-Taylor;
1999	5	Inference For The Generalization Error IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Claude Nadeau; Yoshua Bengio;
1999	6	The Infinite Gaussian Mixture Model IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Carl Edward Rasmussen;
1999	7	A Variational Baysian Framework For Graphical Models IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Hagai Attias;
1999	8	The Relevance Vector Machine IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael E. Tipping;
1999	9	Boosting Algorithms As Gradient Descent IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Llew Mason; Jonathan Baxter; Peter L. Bartlett; Marcus R. Frean;
1999	10	Correctness Of Belief Propagation In Gaussian Graphical Models Of Arbitrary Topology IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yair Weiss; William T. Freeman;
1999	11	Bayesian Map Learning In Dynamic Environments IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Kevin P. Murphy;
1999	12	Scale Mixtures Of Gaussians And The Statistics Of Natural Images IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Martin J. Wainwright; Eero P. Simoncelli;
1999	13	Variational Inference For Bayesian Mixtures Of Factor Analysers IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Zoubin Ghahramani; Matthew J. Beal;
1999	14	Agglomerative Information Bottleneck IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Noam Slonim; Naftali Tishby;
1999	15	Bayesian Network Induction Via Local Neighborhoods IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Dimitris Margaritis; Sebastian Thrun;
1998	1	Exploiting Generative Models In Discriminative Classifiers IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Tommi Jaakkola; David Haussler;
1998	2	Kernel PCA And De-Noising In Feature Spaces IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	SEBASTIAN MIKA et. al.
1998	3	Semi-Supervised Support Vector Machines IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Kristin P. Bennett; Ayhan Demiriz;
1998	4	Support Vector Machines Applied To Face Recognition IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	P. Jonathon Phillips;
1998	5	SMEM Algorithm For Mixture Models IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Naonori Ueda; Ryohei Nakano; Zoubin Ghahramani; Geoffrey E. Hinton;
1998	6	Bayesian PCA IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christopher M. Bishop;
1998	7	Using Analytic QP And Sparseness To Speed Training Of Support Vector Machines IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John C. Platt;
1998	8	Gradient Descent For General Reinforcement Learning IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Leemon C. Baird III; Andrew W. Moore;
1998	9	Finite-Sample Convergence Rates For Q-Learning And Indirect Algorithms IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael J. Kearns; Satinder P. Singh;
1998	10	Learning Nonlinear Dynamical Systems Using An EM Algorithm IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Zoubin Ghahramani; Sam T. Roweis;
1998	11	Bayesian Modeling Of Human Concept Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Joshua B. Tenenbaum;
1998	12	Shrinking The Tube: A New Support Vector Regression Algorithm IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Bernhard Sch�lkopf; Peter L. Bartlett; Alex J. Smola; Robert C. Williamson;
1998	13	Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andrew W. Moore;
1998	14	Dynamically Adapting Kernels In Support Vector Machines IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Nello Cristianini; Colin Campbell; John Shawe-Taylor;
1998	15	Replicator Equations, Maximal Cliques, And Graph Isomorphism IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Marcello Pelillo;
1997	1	Classification By Pairwise Coupling IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Trevor Hastie; Robert Tibshirani;
1997	2	A Framework For Multiple-Instance Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Oded Maron; Tom�s Lozano-P�rez;
1997	3	EM Algorithms For PCA And SPCA IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sam T. Roweis;
1997	4	Reinforcement Learning With Hierarchies Of Machines IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ronald Parr; Stuart J. Russell;
1997	5	Intrusion Detection With Neural Networks IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jake Ryan; Meng-Jang Lin; Risto Miikkulainen;
1997	6	Extended ICA Removes Artifacts From Electroencephalographic Recordings IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	TZYY-PING JUNG et. al.
1997	7	New Approximations Of Differential Entropy For Independent Component Analysis And Projection Pursuit IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Aapo Hyv�rinen;
1997	8	Refractoriness And Neural Precision IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael J. Berry II; Markus Meister;
1997	9	Regression With Input-dependent Noise: A Gaussian Process Treatment IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Paul W. Goldberg; Christopher K. I. Williams; Christopher M. Bishop;
1997	10	Prior Knowledge In Support Vector Kernels IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Bernhard Sch�lkopf; Patrice Simard; Alex J. Smola; Vladimir Vapnik;
1997	11	Mapping A Manifold Of Perceptual Observations IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Joshua B. Tenenbaum;
1997	12	Learning To Order Things IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	William W. Cohen; Robert E. Schapire; Yoram Singer;
1997	13	Independent Component Analysis For Identification Of Artifacts In Magnetoencephalographic Recordings IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ricardo Vig�rio; Veikko Jousm�ki; Matti H�m�l�inen; Riitta Hari; Erkki Oja;
1997	14	Monotonic Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Joseph Sill;
1997	15	The Asymptotic Convergence-Rate Of Q-learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Csaba Szepesv�ri;
1996	1	Support Vector Regression Machines IF:10 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	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 IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Vladimir Vapnik; Steven E. Golowich; Alex J. Smola;
1996	3	Analysis Of Temporal-Diffference Learning With Function Approximation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John N. Tsitsiklis; Benjamin Van Roy;
1996	4	LSTM Can Solve Hard Long Time Lag Problems IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sepp Hochreiter; J�rgen Schmidhuber;
1996	5	Learning From Demonstration IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Stefan Schaal;
1996	6	MIMIC: Finding Optima By Estimating Probability Densities IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jeremy S. De Bonet; Charles Lee Isbell Jr.; Paul A. Viola;
1996	7	Probabilistic Interpretation Of Population Codes IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Richard S. Zemel; Peter Dayan; Alexandre Pouget;
1996	8	Clustering Sequences With Hidden Markov Models IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Padhraic Smyth;
1996	9	Improving The Accuracy And Speed Of Support Vector Machines IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christopher J. C. Burges; Bernhard Sch�lkopf;
1996	10	Practical Confidence And Prediction Intervals IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Tom Heskes;
1996	11	Computing With Infinite Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christopher K. I. Williams;
1996	12	A Mixture Of Experts Classifier With Learning Based On Both Labelled And Unlabelled Data IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David J. Miller; Hasan S. Uyar;
1996	13	Clustering Via Concave Minimization IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Paul S. Bradley; Olvi L. Mangasarian; W. Nick Street;
1996	14	For Valid Generalization The Size Of The Weights Is More Important Than The Size Of The Network IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Peter L. Bartlett;
1996	15	Reinforcement Learning For Dynamic Channel Allocation In Cellular Telephone Systems IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Satinder P. Singh; Dimitri P. Bertsekas;
1995	1	Independent Component Analysis Of Electroencephalographic Data IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Scott Makeig; Anthony J. Bell; Tzyy-Ping Jung; Terrence J. Sejnowski;
1995	2	A New Learning Algorithm For Blind Signal Separation IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Shun-ichi Amari; Andrzej Cichocki; Howard Hua Yang;
1995	3	Factorial Hidden Markov Models IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Zoubin Ghahramani; Michael I. Jordan;
1995	4	Generalization In Reinforcement Learning: Successful Examples Using Sparse Coarse Coding IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Richard S. Sutton;
1995	5	Is Learning The N-th Thing Any Easier Than Learning The First? IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sebastian Thrun;
1995	6	Improving Elevator Performance Using Reinforcement Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Robert H. Crites; Andrew G. Barto;
1995	7	Generalized Learning Vector Quantization IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Atsushi Sato; Keiji Yamada;
1995	8	Human Face Detection In Visual Scenes IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Henry A. Rowley; Shumeet Baluja; Takeo Kanade;
1995	9	Hierarchical Recurrent Neural Networks For Long-Term Dependencies IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Salah El Hihi; Yoshua Bengio;
1995	10	Generating Accurate And Diverse Members Of A Neural-Network Ensemble IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David W. Opitz; Jude W. Shavlik;
1995	11	Extracting Tree-Structured Representations Of Trained Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Mark Craven; Jude W. Shavlik;
1995	12	Exploiting Tractable Substructures In Intractable Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Lawrence K. Saul; Michael I. Jordan;
1995	13	Boosting Decision Trees IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Harris Drucker; Corinna Cortes;
1995	14	Bayesian Methods For Mixtures Of Experts IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steve R. Waterhouse; David MacKay; Anthony J. Robinson;
1995	15	Gaussian Processes For Regression IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christopher K. I. Williams; Carl Edward Rasmussen;
1994	1	Active Learning With Statistical Models IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David A. Cohn; Zoubin Ghahramani; Michael I. Jordan;
1994	2	Neural Network Ensembles, Cross Validation, And Active Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Anders Krogh; Jesper Vedelsby;
1994	3	A Growing Neural Gas Network Learns Topologies IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Bernd Fritzke;
1994	4	A Solvable Connectionist Model Of Immediate Recall Of Ordered Lists IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Neil Burgess;
1994	5	New Algorithms For 2D And 3D Point Matching: Pose Estimation And Correspondence IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steven Gold; Chien-Ping Lu; Anand Rangarajan; Suguna Pappu; Eric Mjolsness;
1994	6	Generalization In Reinforcement Learning: Safely Approximating The Value Function IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Justin A. Boyan; Andrew W. Moore;
1994	7	Convergence Properties Of The K-Means Algorithms IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	L�on Bottou; Yoshua Bengio;
1994	8	Reinforcement Learning Algorithm For Partially Observable Markov Decision Problems IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Tommi Jaakkola; Satinder P. Singh; Michael I. Jordan;
1994	9	Reinforcement Learning With Soft State Aggregation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Satinder P. Singh; Tommi Jaakkola; Michael I. Jordan;
1994	10	Reinforcement Learning Methods For Continuous-Time Markov Decision Problems IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steven J. Bradtke; Michael O. Duff;
1994	11	Inferring Ground Truth From Subjective Labelling Of Venus Images IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Padhraic Smyth; Usama M. Fayyad; Michael C. Burl; Pietro Perona; Pierre Baldi;
1994	12	Learning Many Related Tasks At The Same Time With Backpropagation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Rich Caruana;
1994	13	An Alternative Model For Mixtures Of Experts IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Lei Xu; Michael I. Jordan; Geoffrey E. Hinton;
1994	14	SIMPLIFYING NEURAL NETS BY DISCOVERING FLAT MINIMA IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sepp Hochreiter; J�rgen Schmidhuber;
1994	15	Finding Structure In Reinforcement Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sebastian Thrun; Anton Schwartz;
1993	1	Autoencoders, Minimum Description Length And Helmholtz Free Energy IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Geoffrey E. Hinton; Richard S. Zemel;
1993	2	Statistics Of Natural Images: Scaling In The Woods IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Daniel L. Ruderman; William Bialek;
1993	3	Packet Routing In Dynamically Changing Networks: A Reinforcement Learning Approach IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Justin A. Boyan; Michael L. Littman;
1993	4	Supervised Learning From Incomplete Data Via An EM Approach IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Zoubin Ghahramani; Michael I. Jordan;
1993	5	Neural Network Exploration Using Optimal Experiment Design IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David A. Cohn;
1993	6	When Will A Genetic Algorithm Outperform Hill Climbing IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Melanie Mitchell; John H. Holland; Stephanie Forrest;
1993	7	Hoeffding Races: Accelerating Model Selection Search For Classification And Function Approximation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Oded Maron; Andrew W. Moore;
1993	8	Learning Classification With Unlabeled Data IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Virginia R. de Sa;
1993	9	Bayesian Modeling And Classification Of Neural Signals IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael S. Lewicki;
1993	10	Non-Intrusive Gaze Tracking Using Artificial Neural Networks IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Shumeet Baluja; Dean Pomerleau;
1993	11	The Parti-Game Algorithm For Variable Resolution Reinforcement Learning In Multidimensional State-Spaces IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andrew W. Moore;
1993	12	Learning Curves: Asymptotic Values And Rate Of Convergence IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Corinna Cortes; L. D. Jackel; Sara A. Solla; Vladimir Vapnik; John S. Denker;
1993	13	Optimal Brain Surgeon: Extensions And Performance Comparisons IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Babak Hassibi; David G. Stork; Gregory Wolff;
1993	14	Temporal Difference Learning Of Position Evaluation In The Game Of Go IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Nicol N. Schraudolph; Peter Dayan; Terrence J. Sejnowski;
1993	15	Surface Learning With Applications To Lipreading IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christoph Bregler; Stephen M. Omohundro;
1992	1	Second Order Derivatives For Network Pruning: Optimal Brain Surgeon IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Babak Hassibi; David G. Stork;
1992	2	Feudal Reinforcement Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Peter Dayan; Geoffrey E. Hinton;
1992	3	An Information-Theoretic Approach To Deciphering The Hippocampal Code IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	William E. Skaggs; Bruce L. McNaughton; Katalin M. Gothard;
1992	4	Efficient Pattern Recognition Using A New Transformation Distance IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Patrice Simard; Yann LeCun; John S. Denker;
1992	5	Discriminability-Based Transfer Between Neural Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	L. Y. Pratt;
1992	6	Word Space IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Hinrich Sch�tze;
1992	7	Non-Linear Dimensionality Reduction IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David DeMers; Garrison W. Cottrell;
1992	8	Silicon Auditory Processors As Computer Peripherals IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John Lazzaro; John Wawrzynek; M. Mahowald; Massimo Sivilotti; Dave Gillespie;
1992	9	Bayesian Learning Via Stochastic Dynamics IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Radford M. Neal;
1992	10	Automatic Capacity Tuning Of Very Large VC-Dimension Classifiers IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	I. Guyon; B. Boser; V. Vapnik;
1992	11	Improving Performance In Neural Networks Using A Boosting Algorithm IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Harris Drucker; Robert Schapire; Patrice Simard;
1992	12	A Fast Stochastic Error-Descent Algorithm For Supervised Learning And Optimization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Gert Cauwenberghs;
1992	13	Reinforcement Learning Applied To Linear Quadratic Regulation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steven J. Bradtke;
1992	14	Explanation-Based Neural Network Learning For Robot Control IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Tom M. Mitchell; Sebastian B. Thrun;
1992	15	Information, Prediction, And Query By Committee IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yoav Freund; H. Sebastian Seung; Eli Shamir; Naftali Tishby;
1991	1	A Simple Weight Decay Can Improve Generalization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Anders Krogh; John A. Hertz;
1991	2	Principles Of Risk Minimization For Learning Theory IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	V. Vapnik;
1991	3	Practical Issues In Temporal Difference Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Gerald Tesauro;
1991	4	The Effective Number Of Parameters: An Analysis Of Generalization And Regularization In Nonlinear Learning Systems IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John E. Moody;
1991	5	Unsupervised Learning Of Distributions On Binary Vectors Using Two Layer Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yoav Freund; David Haussler;
1991	6	Tangent Prop – A Formalism For Specifying Selected Invariances In An Adaptive Network IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Patrice Simard; Bernard Victorri; Yann LeCun; John Denker;
1991	7	A Contrast Sensitive Silicon Retina With Reciprocal Synapses IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Kwabena A. Boahen; Andreas G. Andreou;
1991	8	Multi-Digit Recognition Using A Space Displacement Neural Network IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ofer Matan; Christopher J. C. Burges; Yann LeCun; John S. Denker;
1991	9	Improving The Performance Of Radial Basis Function Networks By Learning Center Locations IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Dietrich Wettschereck; Thomas Dietterich;
1991	10	Induction Of Multiscale Temporal Structure IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael C. Mozer;
1991	11	Active Exploration In Dynamic Environments IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Sebastian B. Thrun; Knut M�ller;
1991	12	Hierarchies Of Adaptive Experts IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael I. Jordan; Robert A. Jacobs;
1991	13	Interpretation Of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks Into Rules IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Geoffrey Towell; Jude W. Shavlik;
1991	14	Principled Architecture Selection For Neural Networks: Application To Corporate Bond Rating Prediction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John Moody; Joachim Utans;
1991	15	Structural Risk Minimization For Character Recognition IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	I. Guyon; V. Vapnik; B. Boser; L. Bottou; S. A. Solla;
1990	1	Generalization By Weight-Elimination With Application To Forecasting IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andreas S. Weigend; David E. Rumelhart; Bernardo A. Huberman;
1990	2	SEXNET: A Neural Network Identifies Sex From Human Faces IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	B.A. Golomb; D.T. Lawrence; T.J. Sejnowski;
1990	3	Transforming Neural-Net Output Levels To Probability Distributions IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John S. Denker; Yann LeCun;
1990	4	EMPATH: Face, Emotion, And Gender Recognition Using Holons IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Garrison W. Cottrell; Janet Metcalfe;
1990	5	Spoken Letter Recognition IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Mark Fanty; Ronald Cole;
1990	6	The Recurrent Cascade-Correlation Architecture IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Scott E. Fahlman;
1990	7	Note On Learning Rate Schedules For Stochastic Optimization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christian Darken; John E. Moody;
1990	8	Integrated Segmentation And Recognition Of Hand-Printed Numerals IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	James D. Keeler; David E. Rumelhart; Wee Kheng Leow;
1990	9	A Competitive Modular Connectionist Architecture IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Robert A. Jacobs; Michael I. Jordan;
1990	10	Reinforcement Learning In Markovian And Non-Markovian Environments IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	J�rgen Schmidhuber;
1990	11	Evaluation Of Adaptive Mixtures Of Competing Experts IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steven J. Nowlan; Geoffrey E. Hinton;
1990	12	Second Order Properties Of Error Surfaces: Learning Time And Generalization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yann LeCun; Ido Kanter; Sara A. Solla;
1990	13	A Framework For The Cooperation Of Learning Algorithms IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	L�on Bottou; Patrick Gallinari;
1990	14	Learning Time-varying Concepts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Anthony Kuh; Thomas Petsche; Ronald L. Rivest;
1990	15	Bumptrees For Efficient Function, Constraint And Classification Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Stephen M. Omohundro;
1989	1	Optimal Brain Damage IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yann LeCun; John S. Denker; Sara A. Solla;
1989	2	Handwritten Digit Recognition With A Back-Propagation Network IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	YANN LECUN et. al.
1989	3	The Cascade-Correlation Learning Architecture IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Scott E. Fahlman; Christian Lebiere;
1989	4	Reading A Neural Code IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	William Bialek; Fred Rieke; Robert R. de Ruyter van Steveninck; David Warland;
1989	5	Training Stochastic Model Recognition Algorithms As Networks Can Lead To Maximum Mutual Information Estimation Of Parameters IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John S. Bridle;
1989	6	Training Connectionist Networks With Queries And Selective Sampling IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Les E. Atlas; David A. Cohn; Richard E. Ladner;
1989	7	Maximum Likelihood Competitive Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steven J. Nowlan;
1989	8	Higher Order Recurrent Networks And Grammatical Inference IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	C. Lee Giles; Guo-Zheng Sun; Hsing-Hen Chen; Yee-Chun Lee; Dong Chen;
1989	9	Synergy Of Clustering Multiple Back Propagation Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	William P. Lincoln; Josef Skrzypek;
1989	10	Learning To Control An Unstable System With Forward Modeling IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael I. Jordan; Robert A. Jacobs;
1989	11	Designing Application-Specific Neural Networks Using The Genetic Algorithm IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Steven A. Harp; Tariq Samad; Aloke Guha;
1989	12	An Efficient Implementation Of The Back-propagation Algorithm On The Connection Machine CM-2 IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Xiru Zhang; Michael McKenna; Jill P. Mesirov; David L. Waltz;
1989	13	Algorithms For Better Representation And Faster Learning In Radial Basis Function Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Avijit Saha; James D. Keeler;
1989	14	Using Local Models To Control Movement IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Christopher G. Atkeson;
1989	15	Generalization And Scaling In Reinforcement Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	David H. Ackley; Michael L. Littman;
1988	1	ALVINN: An Autonomous Land Vehicle In A Neural Network IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Dean A. Pomerleau;
1988	2	What Size Net Gives Valid Generalization? IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Eric B. Baum; David Haussler;
1988	3	Training A 3-Node Neural Network Is NP-Complete IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Avrim Blum; Ronald L. Rivest;
1988	4	Skeletonization: A Technique For Trimming The Fat From A Network Via Relevance Assessment IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Michael C. Mozer; Paul Smolensky;
1988	5	Comparing Biases For Minimal Network Construction With Back-Propagation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Stephen Jose Hanson; Lorien Y. Pratt;
1988	6	Winner-Take-All Networks Of O(N) Complexity IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	J. Lazzaro; S. Ryckebusch; M.A. Mahowald; C. A. Mead;
1988	7	Links Between Markov Models And Multilayer Perceptrons IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Herv� Bourlard; C. J. Wellekens;
1988	8	Neural Network Recognizer For Hand-Written Zip Code Digits IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	JOHN S. DENKER et. al.
1988	9	A Back-Propagation Algorithm With Optimal Use Of Hidden Units IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Yves Chauvin;
1988	10	An Application Of The Principle Of Maximum Information Preservation To Linear Systems IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ralph Linsker;
1988	11	Fast Learning In Multi-Resolution Hierarchies IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John Moody;
1988	12	Consonant Recognition By Modular Construction Of Large Phonemic Time-Delay Neural Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Alex Waibel;
1988	13	Efficient Parallel Learning Algorithms For Neural Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Alan H. Kramer; Alberto Sangiovanni-Vincentelli;
1988	14	On The K-Winners-Take-All Network IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	E. Majani; Ruth Erlanson; Yaser S. Abu-Mostafa;
1988	15	Optimization By Mean Field Annealing IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	GRIFF BILBRO et. al.
1987	1	How Neural Nets Work IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Alan S. Lapedes; Robert M. Farber;
1987	2	Supervised Learning Of Probability Distributions By Neural Networks IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Eric B. Baum; Frank Wilczek;
1987	3	Neural Net And Traditional Classifiers IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	William Y. Huang; Richard P. Lippmann;
1987	4	Generalization Of Back Propagation To Recurrent And Higher Order Neural Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Fernando J. Pineda;
1987	5	Constrained Differential Optimization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	John C. Platt; Alan H. Barr;
1987	6	Learning Representations By Recirculation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Geoffrey E. Hinton; James L. McClelland;
1987	7	On The Power Of Neural Networks For Solving Hard Problems IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Jehoshua Bruck; Joseph W. Goodman;
1987	8	Static And Dynamic Error Propagation Networks With Application To Speech Coding IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	A. J. Robinson; F. Fallside;
1987	9	An Optimization Network For Matrix Inversion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Ju-Seog Jang; Soo-Young Lee; Sang-Yung Shin;
1987	10	Encoding Geometric Invariances In Higher-Order Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	C. Lee Giles; R. D. Griffin; T. Maxwell;
1987	11	An Artificial Neural Network For Spatio-Temporal Bipolar Patterns: Application To Phoneme Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Les E. Atlas; Toshiteru Homma; Robert J. Marks II;
1987	12	Phasor Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Andr� J. Noest;
1987	13	MURPHY: A Robot That Learns By Doing IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Bartlett W. Mel;
1987	14	Stochastic Learning Networks And Their Electronic Implementation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	Joshua Alspector; Robert B. Allen; Victor Hu; Srinagesh Satyanarayana;
1987	15	A Dynamical Approach To Temporal Pattern Processing IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save	W. Scott Stornetta; Tad Hogg; Bernardo A. Huberman;