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
Oracle performance for visual captioning
TL;DR: In this paper, the authors investigate the possibility of empirically establishing performance upper bounds on various visual captioning datasets without extra data labelling effort or human evaluation, and demonstrate the construction of such bounds on MS-COCO, YouTube2Text and LSMDC (a combination of M-VAD and MPII-MD).
Posted Content
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions
TL;DR: In this paper, the authors apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling, which achieves or surpasses the state-of-the-art on texture synthesis and inpainting.
Posted Content
Combined Reinforcement Learning via Abstract Representations
TL;DR: In this paper, a shared low-dimensional learned encoding of the environment is proposed to capture summarizing abstractions, which leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space.
Posted Content
Underwhelming Generalization Improvements From Controlling Feature Attribution
TL;DR: This work describes a simple method for taking advantage of auxiliary labels, by training networks to ignore the distracting features which may be extracted outside of the region of interest, on the training images for which such masks are available.
Proceedings Article
Knowledge Matters: Importance of Prior Information for Optimization
Caglar Gulcehre,Yoshua Bengio +1 more
TL;DR: In this paper, the authors explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-theart machine learning algorithms tested failed to learn.