Yoshua Bengio

Research topics

• Neural Networks and Applications · 3 papers
• Domain Adaptation and Few-Shot Learning · 2 papers
• Handwritten Text Recognition Techniques · 1 paper
• Image Processing and 3D Reconstruction · 1 paper
• Face and Expression Recognition · 1 paper
• Machine Learning and Data Classification · 1 paper

Frequent co-authors

• Yann LeCun · 1 paper
• Léon Bottou · 1 paper
• Patrick Haffner · 1 paper
• Aaron C. Courville · 1 paper
• P. Simard · 1 paper
• Paolo Frasconi · 1 paper

Papers

1. Gradient-based learning applied to document recognitionProceedings of the IEEE · 1998 · 59,951 citations

   Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional…

2. Representation Learning: A Review and New PerspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence · 2013 · 13,369 citations

   The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge…

3. Learning long-term dependencies with gradient descent is difficultIEEE Transactions on Neural Networks · 1994 · 8,719 citations

   Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies…