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Ryan Sepassi

3 accepted papers

2020

Model Based Reinforcement Learning for Atari

ICLR 2020spotlight

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How…

Cited by 1127SourcecodeScholar
2018

Generating Wikipedia by Summarizing Long Sequences

ICLR 2018poster

We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a deco…

2018

Mesh-TensorFlow: Deep Learning for Supercomputers

NeurIPS 2018poster

Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very…