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Deep Learning algorithms attempt to discover good representations, at multiple levels of abstraction. There has been rapid progress in this area in recent years, both in terms of algorithms and in terms of applications, but many challenges remain. The workshop aims at bringing together researchers in that field and discussing these challenges, brainstorming about new solutions.
Author Information
Yoshua Bengio (University of Montreal)
Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.
Hugo Larochelle (Twitter)
Russ Salakhutdinov (Carnegie Mellon University)
Tomas Mikolov (Google Research)
Matthew D Zeiler (NYU / Clarifai)
David Mcallester (Toyota Tech Institute Chicago)
Nando de Freitas (University of Oxford)
Josh Tenenbaum (MIT)
Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).
Jian Zhou (University of Texas Southwestern Medical Center)
Assistant Professor, Lyda Hill Department of Bioinformatics, UT Southwestern CPRIT Scholar in Cancer Research, Cancer Prevention and Research Institute of Texas
Volodymyr Mnih (DeepMind)
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Zhiting Hu · Andrew Wilson · Chelsea Finn · Lisa Lee · Taylor Berg-Kirkpatrick · Ruslan Salakhutdinov · Eric Xing -
2019 Poster: XLNet: Generalized Autoregressive Pretraining for Language Understanding »
Zhilin Yang · Zihang Dai · Yiming Yang · Jaime Carbonell · Russ Salakhutdinov · Quoc V Le -
2019 Poster: Write, Execute, Assess: Program Synthesis with a REPL »
Kevin Ellis · Maxwell Nye · Yewen Pu · Felix Sosa · Josh Tenenbaum · Armando Solar-Lezama -
2019 Poster: ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models »
Andrei Barbu · David Mayo · Julian Alverio · William Luo · Christopher Wang · Dan Gutfreund · Josh Tenenbaum · Boris Katz -
2019 Oral: XLNet: Generalized Autoregressive Pretraining for Language Understanding »
Zhilin Yang · Zihang Dai · Yiming Yang · Jaime Carbonell · Russ Salakhutdinov · Quoc V Le -
2019 Poster: Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations »
Kevin Smith · Lingjie Mei · Shunyu Yao · Jiajun Wu · Elizabeth Spelke · Josh Tenenbaum · Tomer Ullman -
2019 Poster: Learning Neural Networks with Adaptive Regularization »
Han Zhao · Yao-Hung Hubert Tsai · Russ Salakhutdinov · Geoffrey Gordon -
2019 Poster: Search on the Replay Buffer: Bridging Planning and Reinforcement Learning »
Benjamin Eysenbach · Russ Salakhutdinov · Sergey Levine -
2019 Poster: Learning Data Manipulation for Augmentation and Weighting »
Zhiting Hu · Bowen Tan · Russ Salakhutdinov · Tom Mitchell · Eric Xing -
2019 Poster: Visual Concept-Metaconcept Learning »
Chi Han · Jiayuan Mao · Chuang Gan · Josh Tenenbaum · Jiajun Wu -
2019 Poster: Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels »
Simon Du · Kangcheng Hou · Russ Salakhutdinov · Barnabas Poczos · Ruosong Wang · Keyulu Xu -
2019 Poster: Mixtape: Breaking the Softmax Bottleneck Efficiently »
Zhilin Yang · Thang Luong · Russ Salakhutdinov · Quoc V Le -
2019 Poster: Finding Friend and Foe in Multi-Agent Games »
Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum -
2019 Spotlight: Finding Friend and Foe in Multi-Agent Games »
Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum -
2019 Poster: Deep Gamblers: Learning to Abstain with Portfolio Theory »
Liu Ziyin · Zhikang Wang · Paul Pu Liang · Russ Salakhutdinov · Louis-Philippe Morency · Masahito Ueda -
2019 Poster: Unsupervised Learning of Object Keypoints for Perception and Control »
Tejas Kulkarni · Ankush Gupta · Catalin Ionescu · Sebastian Borgeaud · Malcolm Reynolds · Andrew Zisserman · Volodymyr Mnih -
2019 Poster: Multiple Futures Prediction »
Yichuan Charlie Tang · Russ Salakhutdinov -
2019 Poster: On Exact Computation with an Infinitely Wide Neural Net »
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang -
2019 Spotlight: On Exact Computation with an Infinitely Wide Neural Net »
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang -
2018 : Opening Remarks: Josh Tenenbaum »
Josh Tenenbaum -
2018 Workshop: Modeling the Physical World: Learning, Perception, and Control »
Jiajun Wu · Kelsey Allen · Kevin Smith · Jessica Hamrick · Emmanuel Dupoux · Marc Toussaint · Josh Tenenbaum -
2018 Poster: Learning to Reconstruct Shapes from Unseen Classes »
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu -
2018 Poster: Learning to Infer Graphics Programs from Hand-Drawn Images »
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum -
2018 Poster: Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction »
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum -
2018 Oral: Learning to Reconstruct Shapes from Unseen Classes »
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu -
2018 Spotlight: Learning to Infer Graphics Programs from Hand-Drawn Images »
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum -
2018 Spotlight: Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction »
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum -
2018 Poster: Visual Object Networks: Image Generation with Disentangled 3D Representations »
Jun-Yan Zhu · Zhoutong Zhang · Chengkai Zhang · Jiajun Wu · Antonio Torralba · Josh Tenenbaum · Bill Freeman -
2018 Poster: How Many Samples are Needed to Estimate a Convolutional Neural Network? »
Simon Du · Yining Wang · Xiyu Zhai · Sivaraman Balakrishnan · Russ Salakhutdinov · Aarti Singh -
2018 Poster: Learning to Share and Hide Intentions using Information Regularization »
DJ Strouse · Max Kleiman-Weiner · Josh Tenenbaum · Matt Botvinick · David Schwab -
2018 Poster: Learning to Exploit Stability for 3D Scene Parsing »
Yilun Du · Zhijian Liu · Hector Basevi · Ales Leonardis · Bill Freeman · Josh Tenenbaum · Jiajun Wu -
2018 Poster: End-to-End Differentiable Physics for Learning and Control »
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter -
2018 Poster: Deep Generative Models with Learnable Knowledge Constraints »
Zhiting Hu · Zichao Yang · Russ Salakhutdinov · LIANHUI Qin · Xiaodan Liang · Haoye Dong · Eric Xing -
2018 Spotlight: End-to-End Differentiable Physics for Learning and Control »
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter -
2018 Poster: Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding »
Kexin Yi · Jiajun Wu · Chuang Gan · Antonio Torralba · Pushmeet Kohli · Josh Tenenbaum -
2018 Poster: 3D-Aware Scene Manipulation via Inverse Graphics »
Shunyu Yao · Tzu Ming Hsu · Jun-Yan Zhu · Jiajun Wu · Antonio Torralba · Bill Freeman · Josh Tenenbaum -
2018 Spotlight: Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding »
Kexin Yi · Jiajun Wu · Chuang Gan · Antonio Torralba · Pushmeet Kohli · Josh Tenenbaum -
2018 Poster: Flexible neural representation for physics prediction »
Damian Mrowca · Chengxu Zhuang · Elias Wang · Nick Haber · Li Fei-Fei · Josh Tenenbaum · Daniel Yamins -
2018 Poster: GLoMo: Unsupervised Learning of Transferable Relational Graphs »
Zhilin Yang · Jake Zhao · Bhuwan Dhingra · Kaiming He · William Cohen · Russ Salakhutdinov · Yann LeCun -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : From deep learning of disentangled representations to higher-level cognition »
Yoshua Bengio -
2017 : Deep Kernel Learning »
Ruslan Salakhutdinov -
2017 : Learn to learn high-dimensional models from few examples »
Josh Tenenbaum -
2017 : Welcome: Josh Tenenbaum »
Josh Tenenbaum -
2017 Workshop: Workshop on Meta-Learning »
Roberto Calandra · Frank Hutter · Hugo Larochelle · Sergey Levine -
2017 Workshop: Deep Learning: Bridging Theory and Practice »
Sanjeev Arora · Maithra Raghu · Russ Salakhutdinov · Ludwig Schmidt · Oriol Vinyals -
2017 Workshop: Learning Disentangled Features: from Perception to Control »
Emily Denton · Siddharth Narayanaswamy · Tejas Kulkarni · Honglak Lee · Diane Bouchacourt · Josh Tenenbaum · David Pfau -
2017 : Panel: "How can we characterise the landscape of intelligent systems and locate human-like intelligence in it?" »
Josh Tenenbaum · Gary Marcus · Katja Hofmann -
2017 : Joshua Tenenbaum: 'Types of intelligence: why human-like AI is important' »
Josh Tenenbaum -
2017 Spotlight: Shape and Material from Sound »
Zhoutong Zhang · Qiujia Li · Zhengjia Huang · Jiajun Wu · Josh Tenenbaum · Bill Freeman -
2017 Spotlight: Scene Physics Acquisition via Visual De-animation »
Jiajun Wu · Erika Lu · Pushmeet Kohli · Bill Freeman · Josh Tenenbaum -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Learning to See Physics via Visual De-animation »
Jiajun Wu · Erika Lu · Pushmeet Kohli · Bill Freeman · Josh Tenenbaum -
2017 Poster: Shape and Material from Sound »
Zhoutong Zhang · Qiujia Li · Zhengjia Huang · Jiajun Wu · Josh Tenenbaum · Bill Freeman -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Good Semi-supervised Learning That Requires a Bad GAN »
Zihang Dai · Zhilin Yang · Fan Yang · William Cohen · Ruslan Salakhutdinov -
2017 Poster: MarrNet: 3D Shape Reconstruction via 2.5D Sketches »
Jiajun Wu · Yifan Wang · Tianfan Xue · Xingyuan Sun · Bill Freeman · Josh Tenenbaum -
2017 Poster: Self-Supervised Intrinsic Image Decomposition »
Michael Janner · Jiajun Wu · Tejas Kulkarni · Ilker Yildirim · Josh Tenenbaum -
2017 Poster: Exploring Generalization in Deep Learning »
Behnam Neyshabur · Srinadh Bhojanapalli · David Mcallester · Nati Srebro -
2017 Tutorial: Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning »
Josh Tenenbaum · Vikash Mansinghka -
2016 : Panel Discussion »
Gisbert Schneider · Ross E Goodwin · Simon Colton · Russ Salakhutdinov · Thorsten Joachims · Florian Pinel -
2016 : Multiplicative and Fine-grained Gating for Reading Comprehension »
Russ Salakhutdinov -
2016 : Datasets, Methodology, and Challenges in Intuitive Physics »
Emmanuel Dupoux · Josh Tenenbaum -
2016 : Josh Tenenbaum »
Josh Tenenbaum -
2016 : Reverse engineering human cooperation (or, How to build machines that treat people like people) »
Josh Tenenbaum · Max Kleiman-Weiner -
2016 : Naive Physics 101: A Tutorial »
Emmanuel Dupoux · Josh Tenenbaum -
2016 : Opening Remarks »
Josh Tenenbaum -
2016 Workshop: Intuitive Physics »
Adam Lerer · Jiajun Wu · Josh Tenenbaum · Emmanuel Dupoux · Rob Fergus -
2016 Poster: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation »
Tejas Kulkarni · Karthik Narasimhan · Ardavan Saeedi · Josh Tenenbaum -
2016 Poster: Architectural Complexity Measures of Recurrent Neural Networks »
Saizheng Zhang · Yuhuai Wu · Tong Che · Zhouhan Lin · Roland Memisevic · Russ Salakhutdinov · Yoshua Bengio -
2016 Poster: Learning values across many orders of magnitude »
Hado van Hasselt · Arthur Guez · Arthur Guez · Matteo Hessel · Volodymyr Mnih · David Silver -
2016 Poster: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling »
Jiajun Wu · Chengkai Zhang · Tianfan Xue · Bill Freeman · Josh Tenenbaum -
2016 Poster: Iterative Refinement of the Approximate Posterior for Directed Belief Networks »
R Devon Hjelm · Russ Salakhutdinov · Kyunghyun Cho · Nebojsa Jojic · Vince Calhoun · Junyoung Chung -
2016 Poster: Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations »
Behnam Neyshabur · Yuhuai Wu · Russ Salakhutdinov · Nati Srebro -
2016 Poster: On Multiplicative Integration with Recurrent Neural Networks »
Yuhuai Wu · Saizheng Zhang · Ying Zhang · Yoshua Bengio · Russ Salakhutdinov -
2016 Poster: Using Fast Weights to Attend to the Recent Past »
Jimmy Ba · Geoffrey E Hinton · Volodymyr Mnih · Joel Leibo · Catalin Ionescu -
2016 Oral: Using Fast Weights to Attend to the Recent Past »
Jimmy Ba · Geoffrey E Hinton · Volodymyr Mnih · Joel Leibo · Catalin Ionescu -
2016 Poster: Learning to Communicate with Deep Multi-Agent Reinforcement Learning »
Jakob Foerster · Yannis Assael · Nando de Freitas · Shimon Whiteson -
2016 Poster: Review Networks for Caption Generation »
Zhilin Yang · Ye Yuan · Yuexin Wu · William Cohen · Russ Salakhutdinov -
2016 Poster: Sampling for Bayesian Program Learning »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing -
2016 Poster: Strategic Attentive Writer for Learning Macro-Actions »
Alexander (Sasha) Vezhnevets · Volodymyr Mnih · Simon Osindero · Alex Graves · Oriol Vinyals · John Agapiou · koray kavukcuoglu -
2016 Poster: Probing the Compositionality of Intuitive Functions »
Eric Schulz · Josh Tenenbaum · David Duvenaud · Maarten Speekenbrink · Samuel J Gershman -
2015 : Importance Weighted Autoencoders »
Russ Salakhutdinov -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 : Generating Images from Captions with Attention »
Russ Salakhutdinov -
2015 : Discussion Panel with Morning Speakers (Day 1) »
Pedro Domingos · Stephen H Muggleton · Rina Dechter · Josh Tenenbaum -
2015 : Cognitive Foundations for Common-Sense Knowledge Representation and Reasoning »
Josh Tenenbaum -
2015 : The Deep Reinforcement Learning Boom »
Volodymyr Mnih -
2015 Poster: Skip-Thought Vectors »
Jamie Kiros · Yukun Zhu · Russ Salakhutdinov · Richard Zemel · Raquel Urtasun · Antonio Torralba · Sanja Fidler -
2015 Poster: Softstar: Heuristic-Guided Probabilistic Inference »
Mathew Monfort · Brenden M Lake · Brenden Lake · Brian Ziebart · Patrick Lucey · Josh Tenenbaum -
2015 Poster: Deep Convolutional Inverse Graphics Network »
Tejas Kulkarni · William Whitney · Pushmeet Kohli · Josh Tenenbaum -
2015 Poster: Learning Wake-Sleep Recurrent Attention Models »
Jimmy Ba · Russ Salakhutdinov · Roger Grosse · Brendan J Frey -
2015 Spotlight: Learning Wake-Sleep Recurrent Attention Models »
Jimmy Ba · Russ Salakhutdinov · Roger Grosse · Brendan J Frey -
2015 Spotlight: Deep Convolutional Inverse Graphics Network »
Tejas Kulkarni · William Whitney · Pushmeet Kohli · Josh Tenenbaum -
2015 Poster: Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning »
Jiajun Wu · Ilker Yildirim · Joseph Lim · Bill Freeman · Josh Tenenbaum -
2015 Poster: Unsupervised Learning by Program Synthesis »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2015 Poster: Path-SGD: Path-Normalized Optimization in Deep Neural Networks »
Behnam Neyshabur · Russ Salakhutdinov · Nati Srebro -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Poster: Recurrent Models of Visual Attention »
Volodymyr Mnih · Nicolas Heess · Alex Graves · koray kavukcuoglu -
2014 Spotlight: Recurrent Models of Visual Attention »
Volodymyr Mnih · Nicolas Heess · Alex Graves · koray kavukcuoglu -
2014 Poster: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio -
2014 Poster: Learning Generative Models with Visual Attention »
Yichuan Charlie Tang · Nitish Srivastava · Russ Salakhutdinov -
2014 Poster: A Multiplicative Model for Learning Distributed Text-Based Attribute Representations »
Jamie Kiros · Richard Zemel · Russ Salakhutdinov -
2014 Poster: Discriminative Metric Learning by Neighborhood Gerrymandering »
Shubhendu Trivedi · David Mcallester · Greg Shakhnarovich -
2014 Poster: Generative Adversarial Nets »
Ian Goodfellow · Jean Pouget-Abadie · Mehdi Mirza · Bing Xu · David Warde-Farley · Sherjil Ozair · Aaron Courville · Yoshua Bengio -
2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio -
2014 Demonstration: Toronto Deep Learning »
Jamie Kiros · Russ Salakhutdinov · Nitish Srivastava · Yichuan Charlie Tang -
2014 Demonstration: Neural Machine Translation »
Bart van Merriënboer · Kyunghyun Cho · Dzmitry Bahdanau · Yoshua Bengio -
2014 Oral: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Oral: Learning Generative Models with Visual Attention »
Yichuan Charlie Tang · Nitish Srivastava · Russ Salakhutdinov -
2014 Session: Oral Session 3 »
Hugo Larochelle -
2014 Poster: An Autoencoder Approach to Learning Bilingual Word Representations »
Sarath Chandar · Stanislas Lauly · Hugo Larochelle · Mitesh Khapra · Balaraman Ravindran · Vikas C Raykar · Amrita Saha -
2014 Poster: Distributed Parameter Estimation in Probabilistic Graphical Models »
Yariv D Mizrahi · Misha Denil · Nando de Freitas -
2014 Poster: Iterative Neural Autoregressive Distribution Estimator NADE-k »
Tapani Raiko · Yao Li · Kyunghyun Cho · Yoshua Bengio -
2013 Workshop: Bayesian Optimization in Theory and Practice »
Matthew Hoffman · Jasper Snoek · Nando de Freitas · Michael A Osborne · Ryan Adams · Sebastien Bubeck · Philipp Hennig · Remi Munos · Andreas Krause -
2013 Workshop: Output Representation Learning »
Yuhong Guo · Dale Schuurmans · Richard Zemel · Samy Bengio · Yoshua Bengio · Li Deng · Dan Roth · Kilian Q Weinberger · Jason Weston · Kihyuk Sohn · Florent Perronnin · Gabriel Synnaeve · Pablo R Strasser · julien audiffren · Carlo Ciliberto · Dan Goldwasser -
2013 Poster: DeViSE: A Deep Visual-Semantic Embedding Model »
Andrea Frome · Greg Corrado · Jonathon Shlens · Samy Bengio · Jeff Dean · Marc'Aurelio Ranzato · Tomas Mikolov -
2013 Session: Spotlight Session 10 »
Hugo Larochelle -
2013 Session: Spotlight Session 9 »
Hugo Larochelle -
2013 Poster: One-shot learning by inverting a compositional causal process »
Brenden M Lake · Russ Salakhutdinov · Josh Tenenbaum -
2013 Demonstration: Distributed Representations of Words and Phrases and their Compositionality »
Tomas Mikolov · Kai Chen · Greg Corrado -
2013 Session: Spotlight Session 8 »
Hugo Larochelle -
2013 Session: Spotlight Session 7 »
Hugo Larochelle -
2013 Session: Spotlight Session 6 »
Hugo Larochelle -
2013 Session: Spotlight Session 5 »
Hugo Larochelle -
2013 Poster: Learning Stochastic Feedforward Neural Networks »
Yichuan Charlie Tang · Russ Salakhutdinov -
2013 Poster: Multi-Prediction Deep Boltzmann Machines »
Ian Goodfellow · Mehdi Mirza · Aaron Courville · Yoshua Bengio -
2013 Poster: Generalized Denoising Auto-Encoders as Generative Models »
Yoshua Bengio · Li Yao · Guillaume Alain · Pascal Vincent -
2013 Poster: Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs »
Yann Dauphin · Yoshua Bengio -
2013 Poster: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2013 Poster: Discriminative Transfer Learning with Tree-based Priors »
Nitish Srivastava · Russ Salakhutdinov -
2013 Poster: RNADE: The real-valued neural autoregressive density-estimator »
Benigno Uria · Iain Murray · Hugo Larochelle -
2013 Poster: Annealing between distributions by averaging moments »
Roger Grosse · Chris Maddison · Russ Salakhutdinov -
2013 Poster: Distributed Representations of Words and Phrases and their Compositionality »
Tomas Mikolov · Ilya Sutskever · Kai Chen · Greg Corrado · Jeff Dean -
2013 Session: Spotlight Session 4 »
Hugo Larochelle -
2013 Oral: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2013 Oral: Annealing between distributions by averaging moments »
Roger Grosse · Chris Maddison · Russ Salakhutdinov -
2013 Session: Spotlight Session 3 »
Hugo Larochelle -
2013 Session: Spotlight Session 2 »
Hugo Larochelle -
2013 Session: Spotlight Session 1 »
Hugo Larochelle -
2013 Poster: The Power of Asymmetry in Binary Hashing »
Behnam Neyshabur · Nati Srebro · Russ Salakhutdinov · Yury Makarychev · Payman Yadollahpour -
2012 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · James Bergstra · Quoc V. Le -
2012 Poster: Hamming Distance Metric Learning »
Mohammad Norouzi · Russ Salakhutdinov · David Fleet -
2012 Poster: A Neural Autoregressive Topic Model »
Hugo Larochelle · Stanislas Lauly -
2012 Poster: Matrix reconstruction with the local max norm »
Rina Foygel · Nati Srebro · Russ Salakhutdinov -
2012 Poster: Multimodal Learning with Deep Boltzmann Machines »
Nitish Srivastava · Russ Salakhutdinov -
2012 Poster: A Better Way to Pre-Train Deep Boltzmann Machines »
Russ Salakhutdinov · Geoffrey E Hinton -
2012 Oral: Multimodal Learning with Deep Boltzmann Machines »
Nitish Srivastava · Russ Salakhutdinov -
2012 Poster: Cardinality Restricted Boltzmann Machines »
Kevin Swersky · Danny Tarlow · Ilya Sutskever · Richard Zemel · Russ Salakhutdinov · Ryan Adams -
2012 Poster: Practical Bayesian Optimization of Machine Learning Algorithms »
Jasper Snoek · Hugo Larochelle · Ryan Adams -
2011 Workshop: Challenges in Learning Hierarchical Models: Transfer Learning and Optimization »
Quoc V. Le · Marc'Aurelio Ranzato · Russ Salakhutdinov · Josh Tenenbaum · Andrew Y Ng -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · Adam Coates · Yann LeCun · Nicolas Le Roux · Andrew Y Ng -
2011 Workshop: Bayesian optimization, experimental design and bandits: Theory and applications »
Nando de Freitas · Roman Garnett · Frank R Hutter · Michael A Osborne -
2011 Oral: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Poster: Object Detection with Grammar Models »
Ross B Girshick · Pedro Felzenszwalb · David Mcallester -
2011 Poster: Shallow vs. Deep Sum-Product Networks »
Olivier Delalleau · Yoshua Bengio -
2011 Poster: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Spotlight: Object Detection with Grammar Models »
Ross B Girshick · Pedro Felzenszwalb · David Mcallester -
2011 Spotlight: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Poster: Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss »
David Mcallester · Joseph Keshet -
2011 Oral: Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss »
David Mcallester · Joseph Keshet -
2011 Poster: Algorithms for Hyper-Parameter Optimization »
James Bergstra · Rémi Bardenet · Yoshua Bengio · Balázs Kégl -
2011 Poster: Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines »
Matthew D Zeiler · Graham Taylor · Leonid Sigal · Iain Matthews · Rob Fergus -
2011 Poster: Learning with the weighted trace-norm under arbitrary sampling distributions »
Rina Foygel · Russ Salakhutdinov · Ohad Shamir · Nati Srebro -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2011 Poster: Transfer Learning by Borrowing Examples »
Joseph Lim · Russ Salakhutdinov · Antonio Torralba -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2010 Invited Talk: How to Grow a Mind: Statistics, Structure and Abstraction »
Josh Tenenbaum -
2010 Oral: Learning to combine foveal glimpses with a third-order Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton -
2010 Session: Spotlights Session 10 »
Nando de Freitas -
2010 Session: Oral Session 12 »
Nando de Freitas -
2010 Poster: Dynamic Infinite Relational Model for Time-varying Relational Data Analysis »
Katsuhiko Ishiguro · Tomoharu Iwata · Naonori Ueda · Josh Tenenbaum -
2010 Poster: Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm »
Russ Salakhutdinov · Nati Srebro -
2010 Poster: Learning to combine foveal glimpses with a third-order Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton -
2010 Poster: Generating more realistic images using gated MRF's »
Marc'Aurelio Ranzato · Volodymyr Mnih · Geoffrey E Hinton -
2010 Poster: Practical Large-Scale Optimization for Max-norm Regularization »
Jason D Lee · Benjamin Recht · Russ Salakhutdinov · Nati Srebro · Joel A Tropp -
2010 Poster: Nonparametric Bayesian Policy Priors for Reinforcement Learning »
Finale P Doshi-Velez · David Wingate · Nicholas Roy · Josh Tenenbaum -
2009 Workshop: Approximate Learning of Large Scale Graphical Models »
Russ Salakhutdinov · Amir Globerson · David Sontag -
2009 Workshop: Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain »
Noah Goodman · Edward Vul · Tom Griffiths · Josh Tenenbaum -
2009 Workshop: Adaptive Sensing, Active Learning, and Experimental Design »
Rui M Castro · Nando de Freitas · Ruben Martinez-Cantin -
2009 Workshop: Analyzing Networks and Learning With Graphs »
Edo M Airoldi · Jure Leskovec · Jon Kleinberg · Josh Tenenbaum -
2009 Poster: Replicated Softmax: an Undirected Topic Model »
Russ Salakhutdinov · Geoffrey E Hinton -
2009 Poster: Slow, Decorrelated Features for Pretraining Complex Cell-like Networks »
James Bergstra · Yoshua Bengio -
2009 Poster: Perceptual Multistability as Markov Chain Monte Carlo Inference »
Samuel J Gershman · Edward Vul · Josh Tenenbaum -
2009 Poster: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Poster: Help or Hinder: Bayesian Models of Social Goal Inference »
Tomer D Ullman · Chris L Baker · Owen Macindoe · Owain Evans · Noah Goodman · Josh Tenenbaum -
2009 Spotlight: Perceptual Multistability as Markov Chain Monte Carlo Inference »
Samuel J Gershman · Edward Vul · Josh Tenenbaum -
2009 Poster: Learning in Markov Random Fields using Tempered Transitions »
Russ Salakhutdinov -
2009 Poster: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model »
Edward Vul · Michael C Frank · George Alvarez · Josh Tenenbaum -
2009 Oral: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model »
Edward Vul · Michael C Frank · George Alvarez · Josh Tenenbaum -
2009 Session: Debate on Future Publication Models for the NIPS Community »
Yoshua Bengio -
2009 Poster: Modelling Relational Data using Bayesian Clustered Tensor Factorization »
Ilya Sutskever · Russ Salakhutdinov · Josh Tenenbaum -
2009 Tutorial: Sequential Monte-Carlo Methods »
Arnaud Doucet · Nando de Freitas -
2008 Workshop: Probabilistic Programming: Universal Languages, Systems and Applications »
Daniel Roy · John Winn · David A McAllester · Vikash Mansinghka · Josh Tenenbaum -
2008 Workshop: Machine learning meets human learning »
Nathaniel D Daw · Tom Griffiths · Josh Tenenbaum · Jerry Zhu -
2008 Poster: An interior-point stochastic approximation method and an L1-regularized delta rule »
Peter Carbonetto · Mark Schmidt · Nando de Freitas -
2008 Oral: An interior-point stochastic approximation method and an L1-regularized delta rule »
Peter Carbonetto · Mark Schmidt · Nando de Freitas -
2008 Demonstration: Worio: A Web-Scale Machine Learning System »
Nando de Freitas · Ali Davar -
2008 Poster: Evaluating probabilities under high-dimensional latent variable models »
Iain Murray · Russ Salakhutdinov -
2008 Spotlight: Evaluating probabilities under high-dimensional latent variable models »
Iain Murray · Russ Salakhutdinov -
2007 Workshop: The Grammar of Vision: Probabilistic Grammar-Based Models for Visual Scene Understanding and Object Categorization »
Virginia Savova · Josh Tenenbaum · Leslie Kaelbling · Alan Yuille -
2007 Spotlight: A Bayesian Framework for Cross-Situational Word-Learning »
Michael C Frank · Noah Goodman · Josh Tenenbaum -
2007 Poster: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Poster: A Bayesian Framework for Cross-Situational Word-Learning »
Michael C Frank · Noah Goodman · Josh Tenenbaum -
2007 Poster: A complexity measure for intuitive theories »
Charles Kemp · Noah Goodman · Josh Tenenbaum -
2007 Poster: Learning the 2-D Topology of Images »
Nicolas Le Roux · Yoshua Bengio · Pascal Lamblin · Marc Joliveau · Balázs Kégl -
2007 Spotlight: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Poster: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Oral: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Spotlight: Bayesian Policy Learning with Trans-Dimensional MCMC »
Matthew Hoffman · Arnaud Doucet · Nando de Freitas · Ajay Jasra -
2007 Poster: Bayesian Policy Learning with Trans-Dimensional MCMC »
Matthew Hoffman · Arnaud Doucet · Nando de Freitas · Ajay Jasra -
2007 Poster: Topmoumoute Online Natural Gradient Algorithm »
Nicolas Le Roux · Pierre-Antoine Manzagol · Yoshua Bengio -
2007 Poster: Active Preference Learning with Discrete Choice Data »
Eric Brochu · Nando de Freitas · Abhijeet Ghosh -
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton -
2006 Poster: Combining causal and similarity-based reasoning »
Charles Kemp · Patrick Shafto · Allison Berke · Josh Tenenbaum -
2006 Poster: Multiple timescales and uncertainty in motor adaptation »
Konrad P Kording · Josh Tenenbaum · Reza Shadmehr -
2006 Poster: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Poster: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Talk: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Talk: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Spotlight: Multiple timescales and uncertainty in motor adaptation »
Konrad P Kording · Josh Tenenbaum · Reza Shadmehr -
2006 Talk: Combining causal and similarity-based reasoning »
Charles Kemp · Patrick Shafto · Allison Berke · Josh Tenenbaum -
2006 Poster: Conditional mean field »
Peter Carbonetto · Nando de Freitas -
2006 Poster: Causal inference in sensorimotor integration »
Konrad P Kording · Josh Tenenbaum -
2006 Tutorial: Bayesian Models of Human Learning and Inference »
Josh Tenenbaum