1.6k citations · 2.7k across the 33 of their papers we have counts for
5 papers · 2 filters
Tensor Regression Networks with various Low-Rank Tensor Approximations
Xingwei Cao, Guillaume Rabusseau
Tensor regression networks achieve high compression rate of neural networks while having slight impact on performances. They do so by imposing low tensor rank structure on the weig…
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon +3
Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for…
Deep Reinforcement Learning that Matters
Peter Henderson, Riashat Islam, Philip Bachman +3
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and acc…
Independently Controllable Factors
Valentin Thomas, Jules Pondard, Emmanuel Bengio +6
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of traini…
Independently Controllable Features
Emmanuel Bengio, Valentin Thomas, Joelle Pineau +2
Finding features that disentangle the different causes of variation in real data is a difficult task, that has nonetheless received considerable attention in static domains like na…