Y
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
More filters
Journal ArticleDOI
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
TL;DR: MetaRLBO is proposed where an autoregressive generative model via Meta-Reinforcement Learning is trained to propose promising sequences for selection via Bayesian Optimization and meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.
Posted Content
Stochastic Gradient Descent on a Portfolio Management Training Criterion Using the IPA Gradient Estimator
Christian Dorion,Yoshua Bengio +1 more
TL;DR: In this article, the authors prove that the investment decision is a Markovian decision process which is Lipschitz continuous almost surely in its parameters, and they use the gradient estimator, obtained by the classical backpropagation algorithm, to converge to a local maximum of their learning criterion.
Proceedings Article
Fast And Slow Learning Of Recurrent Independent Mechanisms
TL;DR: In this article, an attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences.
Proceedings ArticleDOI
Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks
TL;DR: This work proposes a combination of a pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, by replacing the complex convolutionAL operation by a low-cost multiplexer.
Book ChapterDOI
Radial Basis Functions for Speech Recognition
TL;DR: Results of several experiments with these networks on the recognition of phonemes for the TIMIT database are presented, including an experiment on a recurrent network of RBFs.