受强制性开放获取政策约束的文章 - Peter Jin了解详情
可在其他位置公开访问的文章：4 篇
Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving
B Wu, F Iandola, PH Jin, K Keutzer
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
强制性开放获取政策： US Department of Defense
Shift: A zero flop, zero parameter alternative to spatial convolutions
B Wu, A Wan, X Yue, P Jin, S Zhao, N Golmant, A Gholaminejad, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
强制性开放获取政策： US Department of Defense
Integrated model, batch, and domain parallelism in training neural networks
A Gholami, A Azad, P Jin, K Keutzer, A Buluc
Proceedings of the 30th on Symposium on Parallelism in Algorithms and …, 2018
强制性开放获取政策： US Department of Energy
Regret Minimization for Partially Observable Deep Reinforcement Learning
P Jin, K Keutzer, S Levine
arXiv preprint arXiv:1710.11424, 2017
强制性开放获取政策： US Department of Defense
出版信息和资助信息由计算机程序自动确定