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20162021
most citedJoint Multimodal Learning with Deep Generative Models

125 citations · 280 across the 18 of their papers we have counts for

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Showing 2021Show all

9 papers · 1 filter

cs.LG2021★ 5 cited

Generalized Decision Transformer for Offline Hindsight Information Matching

Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu

How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for p…

cs.AI2021★ 1 cited

Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following

Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki +2

An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to in…

cs.CL2021

AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages

Machel Reid, Junjie Hu, Graham Neubig +1

Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource…

cs.AI2021

Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL

Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo

There is considerable interest in designing meta-reinforcement learning (meta-RL) algorithms, which enable autonomous agents to adapt new tasks from small amount of experience. In…

cs.LG2021

Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning

Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima +2

Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementatio…

cs.LG2021

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno +4

Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In…