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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…
Ethical Challenges in Data-Driven Dialogue Systems
Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier +4
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that a…
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…
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…
A Deep Reinforcement Learning Chatbot
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…