Convolutional Monte Carlo Rollouts in Go. - Peter H. Jin; Kurt Keutzer (CoRR, 2015)

How to scale distributed deep learning? - Peter H. Jin; Qiaochu Yuan; Forrest N. Iandola; Kurt Keutzer (CoRR, 2016)

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving. - Bichen Wu; Forrest N. Iandola; Peter H. Jin; Kurt Keutzer (CVPR Workshops, 2017)

SqueezeNext: Hardware-Aware Neural Network Design. - Amir Gholami; Kiseok Kwon; Bichen Wu; Zizheng Tai; Xiangyu Yue 001; Peter H. Jin; Sicheng Zhao; Kurt Keutzer (CVPR Workshops, 2018)

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions. - Bichen Wu; Alvin Wan; Xiangyu Yue 001; Peter H. Jin; Sicheng Zhao; Noah Golmant; Amir Gholaminejad; Joseph Gonzalez 001; Kurt Keutzer (CVPR, 2018)

Spatially Parallel Convolutions. - Peter H. Jin; Boris Ginsburg; Kurt Keutzer (ICLR (Workshop), 2018)

Regret Minimization for Partially Observable Deep Reinforcement Learning. - Peter H. Jin; Kurt Keutzer; Sergey Levine (ICML, 2018)

A Novel Domain Adaptation Framework for Medical Image Segmentation. - Amir Gholami; Shashank Subramanian; Varun Shenoy; Naveen Himthani; Xiangyu Yue 001; Sicheng Zhao; Peter H. Jin; George Biros; Kurt Keutzer (BrainLes@MICCAI (2), 2018)

Integrated Model, Batch, and Domain Parallelism in Training Neural Networks. - Amir Gholami; Ariful Azad; Peter H. Jin; Kurt Keutzer; Aydin Buluç (SPAA, 2018)

Learning to Navigate in Visual Environments. - Peter H. Jin (2018)