Peter H. Jin


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.


Regret Minimization for Partially Observable Deep Reinforcement Learning

Peter H. Jin, Sergey Levine, and Kurt Keutzer

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

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

Convolutional Monte Carlo Rollouts in Go

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