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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Adding noise to the input of a model trained with a regularized objective
TL;DR: This work derives the higher order terms of the Taylor expansion and analyzes the coefficients of the regularization terms induced by the noisy input of a parametric function to study the effect of penalizing the Hessian of the mapping function with respect to the input in terms of generalization performance.
Proceedings ArticleDOI
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition.
TL;DR: In this paper, a new layer normalization technique called Dynamic Layer Normalization (DLN) is introduced for adaptive neural acoustic modeling in speech recognition, which dynamically generates the scaling and shifting parameters in layer normalisation.
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Context-Dependent Word Representation for Neural Machine Translation
TL;DR: This paper proposes to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence and proposes to represent special tokens with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors.
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Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Alaaeldin El-Nouby,Shikhar Sharma,Hannes Schulz,R Devon Hjelm,Layla El Asri,Samira Ebrahimi Kahou,Yoshua Bengio,Graham W. Taylor +7 more
TL;DR: This work presents a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation, and shows that the model is able to generate the background, add new objects, and apply simple transformations to existing objects.
Journal ArticleDOI
Decision trees do not generalize to new variations
TL;DR: It is demonstrated formally that decision trees can be seriously hurt by the curse of dimensionality in a sense that is a bit different from other nonparametric statistical methods, but most importantly that they cannot generalize to variations not seen in the training set.