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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.

Papers
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Proceedings ArticleDOI

Reading checks with multilayer graph transformer networks

Yann Le Cun, +2 more
TL;DR: A new machine learning paradigm called multilayer graph transformer network is proposed that extends the applicability of gradient-based learning algorithms to systems composed of modules that take graphs as input and produce graphs as output.
Proceedings ArticleDOI

Biological Sequence Design with GFlowNets

Moksh Jain, +11 more
TL;DR: This work proposes an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful and novel batches with high scoring candidates after each round.
Posted Content

Not All Neural Embeddings are Born Equal

Felix Hill, +6 more
- 02 Oct 2014 - 
TL;DR: It is shown that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role.
Posted Content

Object-Centric Image Generation from Layouts

Tristan Sylvain, +4 more
- 16 Mar 2020 - 
TL;DR: The idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well is started and an object-centric adaptation of the popular Fr{e}chet Inception Distance metric is introduced, that is better suited for multi-object images.
Proceedings ArticleDOI

Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask

Stylianos Ioannis Mimilakis, +5 more
TL;DR: A recurrent inference algorithm, a sparse transformation step to improve the mask generation process, and a learned denoising filter are introduced that learns and optimizes a source-dependent mask and does not need a post processing step.