677 citations · 863 across the 4 of their papers we have counts for
7 papers
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, William Buchwalter
We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For exa…
Batch weight for domain adaptation with mass shift
Mikołaj Bińkowski, R Devon Hjelm, Aaron Courville
Unsupervised domain transfer is the task of transferring or translating samples from a source distribution to a different target distribution. Current solutions unsupervised domain…
Prediction of Progression to Alzheimer's disease with Deep InfoMax
Alex Fedorov, R Devon Hjelm, Anees Abrol +4
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a…
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex Lamb, Devon Hjelm, Yaroslav Ganin +3
Directed latent variable models that formulate the joint distribution as have the advantage of fast and exact sampling. However, these models have the w…
ACtuAL: Actor-Critic Under Adversarial Learning
Anirudh Goyal, Nan Rosemary Ke, Alex Lamb +4
Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs are typically trained end-to-end on real-v…
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Tong Che, Yanran Li, Ruixiang Zhang +4
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather…