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cs.LG2017
Integrated Model, Batch and Domain Parallelism in Training Neural Networks
Amir Gholami, Ariful Azad, Peter Jin +2
We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using min…
cs.LG2017★ 22 cited
Regret Minimization for Partially Observable Deep Reinforcement Learning
Peter Jin, Kurt Keutzer, Sergey Levine
Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning…