677 citations · 915 across the 10 of their papers we have counts for
9 papers · 1 filter
Locality and compositionality in zero-shot learning
Tristan Sylvain, Linda Petrini, Devon Hjelm
In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). In order to well-isolate the importance of these proper…
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Thang Doan, Bogdan Mazoure, Moloud Abdar +3
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, w…
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…
Unsupervised State Representation Learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair +3
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of…
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…
Leveraging exploration in off-policy algorithms via normalizing flows
Bogdan Mazoure, Thang Doan, Audrey Durand +2
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios. Approaches su…