| Don't decay the learning rate, increase the batch size SL Smith, PJ Kindermans, C Ying, QV Le arXiv preprint arXiv:1711.00489, 2017 | 1160 | 2017 |
| Nas-bench-101: Towards reproducible neural architecture search C Ying, A Klein, E Christiansen, E Real, K Murphy, F Hutter International conference on machine learning, 7105-7114, 2019 | 720 | 2019 |
| Image classification at supercomputer scale C Ying, S Kumar, D Chen, T Wang, Y Cheng arXiv preprint arXiv:1811.06992, 2018 | 148 | 2018 |
| Large-batch training for LSTM and beyond Y You, J Hseu, C Ying, J Demmel, K Keutzer, CJ Hsieh Proceedings of the International Conference for High Performance Computing …, 2019 | 96 | 2019 |
| Quoc V Le SL Smith, PJ Kindermans, C Ying A bayesian perspective on generalization and stochastic gradient descent, 2018 | 31 | 2018 |
| Don’t decay the learning rate, increase the batch size. arXiv 2017 SL Smith, PJ Kindermans, C Ying, QV Le arXiv preprint arXiv:1711.00489, 2021 | 29 | 2021 |
| Depth-adaptive computational policies for efficient visual tracking C Ying, K Fragkiadaki Energy Minimization Methods in Computer Vision and Pattern Recognition: 11th …, 2018 | 11 | 2018 |
| Enumerating unique computational graphs via an iterative graph invariant C Ying arXiv preprint arXiv:1902.06192, 2019 | 8 | 2019 |
| Adaptive Depth Computational Policies for Efficient Visual Tracking C Ying, K Fragkiadaki | | |
