Generative adversarial networks
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a c...
Publications
7 shownRepresentation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more...
Deep Learning
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer ga...
Show, Attend and Tell: Neural Image Caption Generation with Visual\n Attention
Inspired by recent work in machine translation and object detection, we\nintroduce an attention based model that automatically learns to describe the\ncontent of images. We desc...
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We descri...
Why Does Unsupervised Pre-training Help Deep Learning?
Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of autoencoder variants with impressive results being...
An empirical evaluation of deep architectures on problems with many factors of variation
Recently, several learning algorithms relying on models with deep architectures have been proposed. Though they have demonstrated impressive performance, to date, they have only...
Frequent Co-Authors
Researcher Info
- h-index
- 7
- Publications
- 7
- Citations
- 45,491
- Institution
- Université de Montréal
External Links
Identifiers
- ORCID
- 0000-0001-6223-0301
Impact Metrics
h-index
7
h-index: Number of publications with at least h citations each.