Slow feature analysis: unsupervised learning of invariances
Neural Computation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Centering Neural Network Gradient Factors
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Bayesian learning for neural networks
Bayesian learning for neural networks
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
A neural probabilistic language model
The Journal of Machine Learning Research
Estimation of Non-Normalized Statistical Models by Score Matching
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Separating Style and Content with Bilinear Models
Neural Computation
A fast learning algorithm for deep belief nets
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Computation
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
Extracting and composing robust features with denoising autoencoders
Proceedings of the 25th international conference on Machine learning
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale deep unsupervised learning using graphics processors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Factored conditional restricted Boltzmann Machines for modeling motion style
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Deep Architectures for AI
Learning Deep Architectures for AI
A connection between score matching and denoising autoencoders
Neural Computation
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Learning algorithms for the classification restricted Boltzmann machine
The Journal of Machine Learning Research
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks
Machine Learning: A Probabilistic Perspective
Machine Learning: A Probabilistic Perspective
Disentangling factors of variation for facial expression recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges.