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The past five years have seen a huge increase in the capabilities of deep neural networks. Maintaining this rate of progress however, faces some steep challenges, and awaits fundamental insights. As our models become more complex, and venture into areas such as unsupervised learning or reinforcement learning, designing improvements becomes more laborious, and success can be brittle and hard to transfer to new settings.
This workshop seeks to highlight recent works that use theory as well as systematic experiments to isolate the fundamental questions that need to be addressed in deep learning. These have helped flesh out core questions on topics such as generalization, adversarial robustness, large batch training, generative adversarial nets, and optimization, and point towards elements of the theory of deep learning that is expected to emerge in the future.
The workshop aims to enhance this confluence of theory and practice, highlighting influential work with these methods, future open directions, and core fundamental problems. There will be an emphasis on discussion, via panels and round tables, to identify future research directions that are promising and tractable.
Sat 8:35 a.m. - 8:45 a.m.
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Opening Remarks
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Talk
)
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Sat 8:45 a.m. - 9:15 a.m.
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Invited Talk #1 (Yoshua Bengio)
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Talk
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Generalization, Memorization and SGD |
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Sat 9:15 a.m. - 9:45 a.m.
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Invited Talk #2 (Ian Goodfellow)
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Talk
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Bridging Theory and Practice of GANs |
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Sat 9:45 a.m. - 10:00 a.m.
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Spotlights 1
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Talk
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1) Generalization in deep nets: the role of distance from initialization 2) Entropy-SG(L)D optimizes the prior of a (valid) PAC-Bayes bound 3) Large Batch Training of DNNs with Layer-wise Adaptive Rate Scaling |
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Sat 10:00 a.m. - 10:30 a.m.
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Invited Talk #3 (Peter Bartlett)
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Talk
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Generalization in Deep Networks |
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Sat 10:30 a.m. - 11:00 a.m.
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Coffee
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Sat 11:00 a.m. - 11:30 a.m.
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Invited Talk #4 (Doina Precup)
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Talk
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Experimental design in Deep Reinforcement Learning |
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Sat 11:30 a.m. - 11:45 a.m.
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Spotlights 2
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Talk
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1) Measuring robustness of NNs via Minimal Adversarial Examples 2) A classification based perspective on GAN-distributions 3) Learning one hidden layer neural nets with landscape design |
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Sat 11:45 a.m. - 1:30 p.m.
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Poster Session 1 and Lunch
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Poster Session
)
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Sumanth Dathathri · Akshay Rangamani · Prakhar Sharma · Aruni RoyChowdhury · Madhu Advani · William Guss · Chulhee Yun · Corentin Hardy · Michele Alberti · Devendra Sachan · Andreas Veit · Takashi Shinozaki · Peter Chin
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Sat 1:30 p.m. - 2:00 p.m.
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Invited Talk #5 (Percy Liang)
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Talk
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Fighting Black Boxes, Adversaries, and Bugs in Deep Learning |
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Sat 2:00 p.m. - 3:00 p.m.
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Contributed Talks 1,2,3,4
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Talk
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1) Don't Decay the Learning Rate, Increase the Batch Size 2) Meta-Learning and Universality: Deep Representations and Gradient Descent Can Approximate Any Learning Algorithm 3) Hyperparameter Optimization: A Spectral Approach 4) Learning Implicit Generative Models with Method of Learned Moments |
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Sat 3:00 p.m. - 4:00 p.m.
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Poster Session 2
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Poster Session
)
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Sat 4:00 p.m. - 4:30 p.m.
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Invited Talk #6 (Sham Kakade)
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Talk
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Towards Bridging Theory and Practice in DeepRL |
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Sat 4:30 p.m. - 5:30 p.m.
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Panel
(
Discussion Panel
)
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Author Information
Sanjeev Arora (Princeton University)
Maithra Raghu (Cornell University and Google Brain)
Russ Salakhutdinov (Carnegie Mellon University)
Ludwig Schmidt (MIT)
Oriol Vinyals (Google DeepMind)
Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.
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Aniruddh Raghu · Daniel Jarrett · Kathleen Lewis · Elias Chaibub Neto · Nicholas Mastronarde · Shazia Akbar · Chun-Hung Chao · Henghui Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adityanarayanan Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Tzu Ming Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang -
2018 : Plenary Talk 1 »
Sanjeev Arora -
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: Insights on representational similarity in neural networks with canonical correlation »
Ari Morcos · Maithra Raghu · Samy Bengio -
2018 Poster: Deep Generative Models with Learnable Knowledge Constraints »
Zhiting Hu · Zichao Yang · Russ Salakhutdinov · LIANHUI Qin · Xiaodan Liang · Haoye Dong · Eric Xing -
2018 Poster: Adversarially Robust Generalization Requires More Data »
Ludwig Schmidt · Shibani Santurkar · Dimitris Tsipras · Kunal Talwar · Aleksander Madry -
2018 Spotlight: Adversarially Robust Generalization Requires More Data »
Ludwig Schmidt · Shibani Santurkar · Dimitris Tsipras · Kunal Talwar · Aleksander Madry -
2018 Poster: Relational recurrent neural networks »
Adam Santoro · Ryan Faulkner · David Raposo · Jack Rae · Mike Chrzanowski · Theophane Weber · Daan Wierstra · Oriol Vinyals · Razvan Pascanu · Timothy Lillicrap -
2018 Poster: GLoMo: Unsupervised Learning of Transferable Relational Graphs »
Zhilin Yang · Jake Zhao · Bhuwan Dhingra · Kaiming He · William Cohen · Russ Salakhutdinov · Yann LeCun -
2017 : Meta Unsupervised Learning »
Oriol Vinyals -
2017 : Deep Kernel Learning »
Ruslan Salakhutdinov -
2017 : Distilling Expensive Simulations with Neural Networks »
Oriol Vinyals -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Poster: Good Semi-supervised Learning That Requires a Bad GAN »
Zihang Dai · Zhilin Yang · Fan Yang · William Cohen · Ruslan Salakhutdinov -
2017 Oral: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Poster: Neural Discrete Representation Learning »
Aaron van den Oord · Oriol Vinyals · koray kavukcuoglu -
2017 Poster: Communication-Efficient Distributed Learning of Discrete Distributions »
Ilias Diakonikolas · Elena Grigorescu · Jerry Li · Abhiram Natarajan · Krzysztof Onak · Ludwig Schmidt -
2017 Oral: Communication-Efficient Distributed Learning of Discrete Distributions »
Ilias Diakonikolas · Elena Grigorescu · Jerry Li · Abhiram Natarajan · Krzysztof Onak · Ludwig Schmidt -
2017 Poster: On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks »
Arturs Backurs · Piotr Indyk · Ludwig Schmidt -
2017 Poster: SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability »
Maithra Raghu · Justin Gilmer · Jason Yosinski · Jascha Sohl-Dickstein -
2017 Tutorial: Deep Learning: Practice and Trends »
Nando de Freitas · Scott Reed · Oriol Vinyals -
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 Poster: Conditional Image Generation with PixelCNN Decoders »
Aaron van den Oord · Nal Kalchbrenner · Lasse Espeholt · koray kavukcuoglu · Oriol Vinyals · Alex Graves -
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: Fast recovery from a union of subspaces »
Chinmay Hegde · Piotr Indyk · Ludwig Schmidt -
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: An Online Sequence-to-Sequence Model Using Partial Conditioning »
Navdeep Jaitly · Quoc V Le · Oriol Vinyals · Ilya Sutskever · David Sussillo · Samy Bengio -
2016 Poster: Exponential expressivity in deep neural networks through transient chaos »
Ben Poole · Subhaneil Lahiri · Maithra Raghu · Jascha Sohl-Dickstein · Surya Ganguli -
2016 Poster: On Multiplicative Integration with Recurrent Neural Networks »
Yuhuai Wu · Saizheng Zhang · Ying Zhang · Yoshua Bengio · Russ Salakhutdinov -
2016 Poster: Review Networks for Caption Generation »
Zhilin Yang · Ye Yuan · Yuexin Wu · William Cohen · Russ Salakhutdinov -
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: Matching Networks for One Shot Learning »
Oriol Vinyals · Charles Blundell · Timothy Lillicrap · koray kavukcuoglu · Daan Wierstra -
2015 : Importance Weighted Autoencoders »
Russ Salakhutdinov -
2015 : Generating Images from Captions with Attention »
Russ Salakhutdinov -
2015 Poster: Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks »
Samy Bengio · Oriol Vinyals · Navdeep Jaitly · Noam Shazeer -
2015 Poster: Skip-Thought Vectors »
Jamie Kiros · Yukun Zhu · Russ Salakhutdinov · Richard Zemel · Raquel Urtasun · Antonio Torralba · Sanja Fidler -
2015 Poster: Practical and Optimal LSH for Angular Distance »
Alexandr Andoni · Piotr Indyk · Thijs Laarhoven · Ilya Razenshteyn · Ludwig Schmidt -
2015 Poster: Learning Wake-Sleep Recurrent Attention Models »
Jimmy Ba · Russ Salakhutdinov · Roger Grosse · Brendan J Frey -
2015 Poster: Pointer Networks »
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly -
2015 Spotlight: Pointer Networks »
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly -
2015 Spotlight: Learning Wake-Sleep Recurrent Attention Models »
Jimmy Ba · Russ Salakhutdinov · Roger Grosse · Brendan J Frey -
2015 Poster: Path-SGD: Path-Normalized Optimization in Deep Neural Networks »
Behnam Neyshabur · Russ Salakhutdinov · Nati Srebro -
2015 Poster: Differentially Private Learning of Structured Discrete Distributions »
Ilias Diakonikolas · Moritz Hardt · Ludwig Schmidt -
2015 Poster: Grammar as a Foreign Language »
Oriol Vinyals · Łukasz Kaiser · Terry Koo · Slav Petrov · Ilya Sutskever · Geoffrey Hinton -
2015 Tutorial: Large-Scale Distributed Systems for Training Neural Networks »
Jeff Dean · Oriol Vinyals -
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 Demonstration: Toronto Deep Learning »
Jamie Kiros · Russ Salakhutdinov · Nitish Srivastava · Yichuan Charlie Tang -
2014 Oral: Learning Generative Models with Visual Attention »
Yichuan Charlie Tang · Nitish Srivastava · Russ Salakhutdinov -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Poster: One-shot learning by inverting a compositional causal process »
Brenden M Lake · Russ Salakhutdinov · Josh Tenenbaum -
2013 Poster: Learning Stochastic Feedforward Neural Networks »
Yichuan Charlie Tang · Russ Salakhutdinov -
2013 Poster: Discriminative Transfer Learning with Tree-based Priors »
Nitish Srivastava · Russ Salakhutdinov -
2013 Poster: Annealing between distributions by averaging moments »
Roger Grosse · Chris Maddison · Russ Salakhutdinov -
2013 Oral: Annealing between distributions by averaging moments »
Roger Grosse · Chris Maddison · Russ Salakhutdinov -
2013 Poster: The Power of Asymmetry in Binary Hashing »
Behnam Neyshabur · Nati Srebro · Russ Salakhutdinov · Yury Makarychev · Payman Yadollahpour -
2012 Poster: Hamming Distance Metric Learning »
Mohammad Norouzi · Russ Salakhutdinov · David Fleet -
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: Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders »
Sanjeev Arora · Rong Ge · Ankur Moitra · Sushant Sachdeva -
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 -
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 Poster: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Spotlight: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Poster: Learning with the weighted trace-norm under arbitrary sampling distributions »
Rina Foygel · Russ Salakhutdinov · Ohad Shamir · Nati Srebro -
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 Poster: Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm »
Russ Salakhutdinov · Nati Srebro -
2010 Poster: Practical Large-Scale Optimization for Max-norm Regularization »
Jason D Lee · Benjamin Recht · Russ Salakhutdinov · Nati Srebro · Joel A Tropp -
2009 Workshop: Approximate Learning of Large Scale Graphical Models »
Russ Salakhutdinov · Amir Globerson · David Sontag -
2009 Poster: Replicated Softmax: an Undirected Topic Model »
Russ Salakhutdinov · Geoffrey E Hinton -
2009 Poster: Learning in Markov Random Fields using Tempered Transitions »
Russ Salakhutdinov -
2009 Poster: Modelling Relational Data using Bayesian Clustered Tensor Factorization »
Ilya Sutskever · Russ Salakhutdinov · Josh Tenenbaum -
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 Poster: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Oral: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton