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.
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A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation
TL;DR: In this paper, an attention-based encoder-decoder with a subword-level encoder and a character-level decoder was proposed to generate a character sequence without explicit segmentation.
Proceedings Article
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
TL;DR: A probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences that outperforms many traditional models of polyphonic music on a variety of realistic datasets is introduced.
Posted Content
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Kundan Kumar,Rithesh Kumar,Thibault de Boissiere,Lucas Gestin,Wei Zhen Teoh,Jose Sotelo,Alexandre de Brebisson,Yoshua Bengio,Aaron Courville +8 more
TL;DR: This article proposed a non-autoregressive, fully convolutional GAN for mel-spectrogram inversion and achieved state-of-the-art performance in speech synthesis, music domain translation and unconditional music synthesis.
Posted Content
Noisy Activation Functions
TL;DR: This work proposes to exploit the injection of appropriate noise so that the gradients may flow easily, even if the noiseless application of the activation function would yield zero gradient, and establishes connections to simulated annealing, making it easier to optimize hard objective functions.
Proceedings Article
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
TL;DR: This work explores the use of tempered Markov Chain Monte-Carlo for sampling in RBMs and finds both through visualization of samples and measures of likelihood that it helps both sampling and learning.