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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
Andrew Y Ng (Baidu Research)
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.
Adam Coates (Baidu Research)
Roland Memisevic (Twenty Billion Neurons)
Sharanyan Chetlur (NVIDIA)
Geoffrey E Hinton (Google & University of Toronto)
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at Carnegie-Mellon where he pioneered back-propagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.
Shamim Nemati (Harvard)
Bryan Catanzaro (NVIDIA)
Surya Ganguli (Stanford)
Herbert Jaeger (Jacobs University Bremen)
Phil Blunsom (Oxford University)
Leon Bottou (Facebook AI Research)
Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .
Volodymyr Mnih (DeepMind)
Chen-Yu Lee (Magic Leap, Inc)
Rich M Schwartz (BBN Technologies)
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2011 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · Adam Coates · Yann LeCun · Nicolas Le Roux · Andrew Y Ng -
2011 Oral: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning »
Quoc V. Le · Alexandre Karpenko · Jiquan Ngiam · Andrew Y Ng -
2011 Poster: Unfolding Recursive Autoencoders for Paraphrase Detection »
Richard Socher · Eric H Huang · Jeffrey Pennin · Andrew Y Ng · Christopher D Manning -
2011 Poster: Shallow vs. Deep Sum-Product Networks »
Olivier Delalleau · Yoshua Bengio -
2011 Poster: Sparse Filtering »
Jiquan Ngiam · Pang Wei Koh · Zhenghao Chen · Sonia A Bhaskar · Andrew Y Ng -
2011 Poster: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Spotlight: Sparse Filtering »
Jiquan Ngiam · Pang Wei Koh · Zhenghao Chen · Sonia A Bhaskar · Andrew Y Ng -
2011 Demonstration: Haptic Belt with Pedestrian Detection »
Jean Feng · Marc Rasi · Andrew Y Ng · Quoc V. Le · Morgan Quigley · Justin K Chen · Tiffany Low · Will Y Zou -
2011 Poster: Algorithms for Hyper-Parameter Optimization »
James Bergstra · Rémi Bardenet · Yoshua Bengio · Balázs Kégl -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2011 Poster: Selecting Receptive Fields in Deep Networks »
Adam Coates · Andrew Y Ng -
2011 Poster: Unsupervised learning models of primary cortical receptive fields and receptive field plasticity »
Andrew M Saxe · Maneesh Bhand · Ritvik Mudur · Bipin Suresh · Andrew Y Ng -
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2010 Talk: A Probabilistic Approach to Data Visualization »
Geoffrey E Hinton -
2010 Oral: Learning to combine foveal glimpses with a third-order Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton -
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: Tiled convolutional neural networks »
Quoc V. Le · Jiquan Ngiam · Zhenghao Chen · Daniel Jin hao Chia · Pang Wei Koh · Andrew Y Ng -
2010 Poster: Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine »
George Dahl · Marc'Aurelio Ranzato · Abdel-rahman Mohamed · Geoffrey E Hinton -
2010 Poster: Gated Softmax Classification »
Roland Memisevic · Christopher Zach · Geoffrey E Hinton · Marc Pollefeys -
2010 Poster: Short-term memory in neuronal networks through dynamical compressed sensing »
Surya Ganguli · Haim Sompolinsky -
2010 Poster: Energy Disaggregation via Discriminative Sparse Coding »
J. Zico Kolter · Siddarth Batra · Andrew Y Ng -
2009 Workshop: Deep Learning for Speech Recognition and Related Applications »
Li Deng · Dong Yu · Geoffrey E Hinton -
2009 Mini Symposium: Machine Learning for Sustainability »
J. Zico Kolter · Thomas Dietterich · Andrew Y Ng -
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: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Poster: Measuring Invariances in Deep Networks »
Ian Goodfellow · Quoc V. Le · Andrew M Saxe · Andrew Y Ng -
2009 Poster: 3D Object Recognition with Deep Belief Nets »
Vinod Nair · Geoffrey E Hinton -
2009 Poster: Unsupervised feature learning for audio classification using convolutional deep belief networks »
Honglak Lee · Peter Pham · Yan Largman · Andrew Y Ng -
2009 Spotlight: 3D Object Recognition with Deep Belief Nets »
Vinod Nair · Geoffrey E Hinton -
2009 Invited Talk: Deep Learning with Multiplicative Interactions »
Geoffrey E Hinton -
2009 Session: Debate on Future Publication Models for the NIPS Community »
Yoshua Bengio -
2009 Poster: Zero-shot Learning with Semantic Output Codes »
Mark M Palatucci · Dean Pomerleau · Geoffrey E Hinton · Tom Mitchell -
2008 Poster: Using matrices to model symbolic relationship »
Ilya Sutskever · Geoffrey E Hinton -
2008 Demonstration: Visualizing NIPS Cooperations using Multiple Maps t-SNE »
Laurens van der Maaten · Geoffrey E Hinton -
2008 Spotlight: Using matrices to model symbolic relationship »
Ilya Sutskever · Geoffrey E Hinton -
2008 Poster: The Recurrent Temporal Restricted Boltzmann Machine »
Ilya Sutskever · Geoffrey E Hinton · Graham Taylor -
2008 Poster: A Scalable Hierarchical Distributed Language Model »
Andriy Mnih · Geoffrey E Hinton -
2008 Demonstration: High-Accuracy 3D Sensing for Mobile Manipulators »
Stephen Gould · Morgan Quigley · Siddarth Batra · Ellen Klingbiel · Quoc V Le · Andrew Y Ng -
2008 Poster: Implicit Mixtures of Restricted Boltzmann Machines »
Vinod Nair · Geoffrey E Hinton -
2008 Poster: Competing RBM density models for classification of fMRI images »
Tanya Schmah · Geoffrey E Hinton · Richard Zemel -
2007 Poster: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Poster: Learning the 2-D Topology of Images »
Nicolas Le Roux · Yoshua Bengio · Pascal Lamblin · Marc Joliveau · Balázs Kégl -
2007 Poster: Sparse deep belief net model for visual area V2 »
Honglak Lee · Ekanadham Chaitanya · Andrew Y Ng -
2007 Spotlight: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Demonstration: Holistic Scene Understanding from Visual and Range Data »
Stephen Gould · Morgan Quigley · Andrew Y Ng · Daphne Koller -
2007 Spotlight: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Poster: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Demonstration: Building a 3-D Model From a Single Still Image »
Ashutosh Saxena · min sun · Andrew Y Ng -
2007 Tutorial: Deep Belief Nets »
Geoffrey E Hinton -
2007 Tutorial: Learning Using Many Examples »
Leon Bottou · Andrew W Moore -
2007 Poster: Topmoumoute Online Natural Gradient Algorithm »
Nicolas Le Roux · Pierre-Antoine Manzagol · Yoshua Bengio -
2007 Poster: The Tradeoffs of Large Scale Learning »
Leon Bottou · Olivier Bousquet -
2007 Poster: Efficient multiple hyperparameter learning for log-linear models »
Chuong B Do · Chuan-Sheng Foo · Andrew Y Ng -
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton -
2007 Poster: Modeling image patches with a directed hierarchy of Markov random fields »
Simon Osindero · Geoffrey E Hinton -
2006 Demonstration: Peripheral-Foveal Vision for Real-time Object Recognition »
Benjamin Sapp · Stephen Gould · Adrian Kaehler · Gary R Bradski · Andrew Y Ng -
2006 Poster: Robotic Grasping of Novel Objects »
Ashutosh Saxena · Justin Driemeyer · Justin Kearns · Andrew Y Ng -
2006 Poster: Modeling Human Motion Using Binary Latent Variables »
Graham Taylor · Geoffrey E Hinton · Sam T Roweis -
2006 Poster: Map-Reduce for Machine Learning on Multicore »
Cheng-Tao Chu · Sang Kyun Kim · Yi-An Lin · YuanYuan Yu · Gary R Bradski · Andrew Y Ng · Kunle Olukotun -
2006 Poster: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Poster: An Application of Reinforcement Learning to Aerobatic Helicopter Flight »
Pieter Abbeel · Adam P Coates · Andrew Y Ng · Morgan Quigley -
2006 Talk: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Talk: Map-Reduce for Machine Learning on Multicore »
Cheng-Tao Chu · Sang Kyun Kim · Yi-An Lin · YuanYuan Yu · Gary R Bradski · Andrew Y Ng · Kunle Olukotun -
2006 Spotlight: Robotic Grasping of Novel Objects »
Ashutosh Saxena · Justin Driemeyer · Justin Kearns · Andrew Y Ng -
2006 Spotlight: Modeling Human Motion Using Binary Latent Variables »
Graham Taylor · Geoffrey E Hinton · Sam T Roweis -
2006 Talk: An Application of Reinforcement Learning to Aerobatic Helicopter Flight »
Pieter Abbeel · Adam P Coates · Andrew Y Ng · Morgan Quigley -
2006 Poster: Efficient sparse coding algorithms, end-stopping and nCRF surround suppression »
Honglak Lee · Alexis Battle · Raina Rajat · Andrew Y Ng