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Yoshua Bengio

Researcher at Université de Montréal

Publications -  1146
Citations -  534376

Yoshua Bengio is an academic researcher from Université de Montréal. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 202, co-authored 1033 publications receiving 420313 citations. Previous affiliations of Yoshua Bengio include McGill University & Centre de Recherches Mathématiques.

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Spectral Dimensionality Reduction

Yoshua Bengio, +5 more
TL;DR: A number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition are put under a common framework.
Posted Content

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation

Rim Assouel, +4 more
- 27 Sep 2018 - 
TL;DR: This work proposes a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph and demonstrates favourable performance of the model on prototype-based molecular graph conditional generation tasks.
Posted Content

Diet Networks: Thin Parameters for Fat Genomics

Adriana Romero, +9 more
- 28 Nov 2016 - 
TL;DR: In this paper, the authors proposed a novel neural network parametrization which considerably reduces the number of free parameters in the first layer of a classifier neural network by learning or providing a distributed representation for each input feature (e.g. for each position in the genome where variations are observed).
Posted Content

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

Stanisław Jastrzębski, +7 more
- 13 Jul 2018 - 
TL;DR: This article studied the SGD dynamics in relation to the sharpest directions in the initial phase and found that SGD step is large compared to the curvature and commonly fails to minimize the loss along these directions.
Proceedings Article

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

Stanisław Jastrzębski, +7 more
TL;DR: The authors studied the SGD dynamics in relation to the sharpest directions in the initial phase and found that SGD step is large compared to the curvature and commonly fails to minimize the loss along these directions.