NIPS Proceedingsβ

Yoshua Bengio

56 Papers

  • 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)