NIPS Proceedings
^{β}
Books
Yoshua Bengio
60 Papers
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
(2017)
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
(2017)
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
(2017)
Z-Forcing: Training Stochastic Recurrent Networks
(2017)
Architectural Complexity Measures of Recurrent Neural Networks
(2016)
Binarized Neural Networks
(2016)
On Multiplicative Integration with Recurrent Neural Networks
(2016)
Professor Forcing: A New Algorithm for Training Recurrent Networks
(2016)
A Recurrent Latent Variable Model for Sequential Data
(2015)
Attention-Based Models for Speech Recognition
(2015)
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
(2015)
Equilibrated adaptive learning rates for non-convex optimization
(2015)
Generative Adversarial Nets
(2014)
How transferable are features in deep neural networks?
(2014)
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
(2014)
Iterative Neural Autoregressive Distribution Estimator NADE-k
(2014)
On the Number of Linear Regions of Deep Neural Networks
(2014)
Generalized Denoising Auto-Encoders as Generative Models
(2013)
Multi-Prediction Deep Boltzmann Machines
(2013)
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs
(2013)
Algorithms for Hyper-Parameter Optimization
(2011)
On Tracking The Partition Function
(2011)
Shallow vs. Deep Sum-Product Networks
(2011)
The Manifold Tangent Classifier
(2011)
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism
(2009)
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks
(2009)
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes
(2007)
Learning the 2-D Topology of Images
(2007)
Topmoumoute Online Natural Gradient Algorithm
(2007)
Greedy Layer-Wise Training of Deep Networks
(2006)
Convex Neural Networks
(2005)
Non-Local Manifold Parzen Windows
(2005)
The Curse of Highly Variable Functions for Local Kernel Machines
(2005)
Brain Inspired Reinforcement Learning
(2004)
Non-Local Manifold Tangent Learning
(2004)
Semi-supervised Learning by Entropy Minimization
(2004)
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
(2003)
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering
(2003)
Manifold Parzen Windows
(2002)
A Parallel Mixture of SVMs for Very Large Scale Problems
(2001)
Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference
(2001)
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms
(2001)
A Neural Probabilistic Language Model
(2000)
Incorporating Second-Order Functional Knowledge for Better Option Pricing
(2000)
Inference for the Generalization Error
(1999)
Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks
(1999)
Shared Context Probabilistic Transducers
(1997)
Training Methods for Adaptive Boosting of Neural Networks
(1997)
Multi-Task Learning for Stock Selection
(1996)
Hierarchical Recurrent Neural Networks for Long-Term Dependencies
(1995)
Recurrent Neural Networks for Missing or Asynchronous Data
(1995)
An Input Output HMM Architecture
(1994)
Convergence Properties of the K-Means Algorithms
(1994)
Diffusion of Credit in Markovian Models
(1994)
Credit Assignment through Time: Alternatives to Backpropagation
(1993)
Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
(1993)
Neural Network - Gaussian Mixture Hybrid for Speech Recognition or Density Estimation
(1991)
A Neural Network to Detect Homologies in Proteins
(1989)
Speaker Independent Speech Recognition with Neural Networks and Speech Knowledge
(1989)
Use of Multi-Layered Networks for Coding Speech with Phonetic Features
(1988)
2 Books
Advances in Neural Information Processing Systems 22
(2009)
Advances in Neural Information Processing Systems 21
(2008)