Our paper was accepted for ICML2021.

【Information】 Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, and Shixiang Shane Gu. “Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning”, International Conference on Machine Learning 2021 (ICML2021). July 2021. 【Overview】 Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing…

Our paper was accepted for EMNLP2020.

Our paper was accepted to the main conference at EMNLP 2020. 【Information】Our paper was accepted to the main conference at EMNLP 2020 【Title】VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling 【Authors】Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo 【Overview】In this paper, we tackle the task of definition modeling, where the goal is…