| 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 | 747 | 2017 |
| 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 | 437 | 2018 |
| Squeezenext: Hardware-aware neural network design A Gholami, K Kwon, B Wu, Z Tai, X Yue, P Jin, S Zhao, K Keutzer Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 365 | 2018 |
| How to scale distributed deep learning? PH Jin, Q Yuan, F Iandola, K Keutzer arXiv preprint arXiv:1611.04581, 2016 | 158 | 2016 |
| 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 | 108* | 2018 |
| A novel domain adaptation framework for medical image segmentation A Gholami, S Subramanian, V Shenoy, N Himthani, X Yue, S Zhao, P Jin, ... International MICCAI Brainlesion Workshop, 289-298, 2018 | 70 | 2018 |
| Regret Minimization for Partially Observable Deep Reinforcement Learning P Jin, K Keutzer, S Levine arXiv preprint arXiv:1710.11424, 2017 | 61 | 2017 |
| Spatially Parallel Convolutions P Jin, B Ginsburg, K Keutzer | 11 | 2018 |
| Convolutional Monte Carlo Rollouts in Go PH Jin, K Keutzer arXiv preprint arXiv:1512.03375, 2015 | 5 | 2015 |
| Learning to Navigate in Visual Environments P Jin UC Berkeley, 2018 | | 2018 |
