Peter H. Jin

cuw@rrpf.orexryrl.rqh

I’m a Computer Science PhD student in the ASPIRE, BAIR, BDD labs, where I’m part of Kurt Keutzer’s group. My current work is on stochastic optimization methods for deep learning, including parallel/distributed learning and new algorithms. I’m also interested in all things related to Monte Carlo tree search and GPUs.

I received my AB in Physics from Princeton University in 2012.

Preprints

Regret Minimization for Partially Observable Deep Reinforcement Learning

Peter H. Jin, Sergey Levine, and Kurt Keutzer
[arxiv]

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

Bichen Wu, Forrest Iandola, Peter H. Jin, and Kurt Keutzer
CVPR Embedded Vision Workshop 2017
[arxiv]

How to scale distributed deep learning? [a.k.a. the gossiping SGD paper]

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, and Kurt Keutzer
NIPS ML Systems Workshop 2016
[arxiv]

Convolutional Monte Carlo Rollouts in Go

Peter H. Jin and Kurt Keutzer
CG 2016 Neural Networks Workshop
[arxiv]