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
Posted Content
Speaker Recognition from Raw Waveform with SincNet
Mirco Ravanelli,Yoshua Bengio +1 more
TL;DR: SincNet as mentioned in this paper is based on parametrized sinc functions, which implement band-pass filters, and only low and high cutoff frequencies are directly learned from data with the proposed method.
Posted Content
Generalization of Equilibrium Propagation to Vector Field Dynamics.
TL;DR: This work presents a simple two-phase learning procedure for fixed point recurrent networks that generalizes Equilibrium Propagation to vector field dynamics, relaxing the requirement of an energy function.
Posted Content
Towards Gene Expression Convolutions using Gene Interaction Graphs
TL;DR: It is found experimentally that there exists non-linear signal in the data, however is it not discovered automatically given the noise and low numbers of samples used in most research, and the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information.
Posted Content
Towards Causal Representation Learning
Bernhard Schölkopf,Francesco Locatello,Stefan Bauer,Nan Rosemary Ke,Nal Kalchbrenner,Anirudh Goyal,Yoshua Bengio +6 more
TL;DR: The authors reviewed fundamental concepts of causal inference and related them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research.
Proceedings Article
Discriminative non-negative matrix factorization for multiple pitch estimation
TL;DR: The idea is to extend the sparse NMF framework by incorporating pitch information present in time-aligned musical scores in order to extract features that enforce the separability between pitch labels.