| Plug-and-play methods provably converge with properly trained denoisers E Ryu, J Liu, S Wang, X Chen, Z Wang, W Yin International Conference on Machine Learning, 5546-5557, 2019 | 392 | 2019 |
| Can we gain more from orthogonality regularizations in training deep networks? N Bansal, X Chen, Z Wang Advances in Neural Information Processing Systems 31, 2018 | 363 | 2018 |
| Drawing early-bird tickets: Towards more efficient training of deep networks H You, C Li, P Xu, Y Fu, Y Wang, X Chen, RG Baraniuk, Z Wang, Y Lin arXiv preprint arXiv:1909.11957, 2019 | 271 | 2019 |
| Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds X Chen, J Liu, Z Wang, W Yin Advances in Neural Information Processing Systems 31, 2018 | 263 | 2018 |
| Learning to optimize: A primer and a benchmark T Chen, X Chen, W Chen, H Heaton, J Liu, Z Wang, W Yin Journal of Machine Learning Research 23 (189), 1-59, 2022 | 214 | 2022 |
| ALISTA: Analytic weights are as good as learned weights in LISTA J Liu, X Chen International Conference on Learning Representations (ICLR), 2019 | 207 | 2019 |
| More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity S Liu, T Chen, X Chen, X Chen, Q Xiao, B Wu, T Kärkkäinen, ... arXiv preprint arXiv:2207.03620, 2022 | 154 | 2022 |
| Sparse Training via Boosting Pruning Plasticity with Neuroregeneration S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ... Neural Information Processing Systems 2021, 2021 | 117 | 2021 |
| The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training S Liu, T Chen, X Chen, L Shen, DC Mocanu, Z Wang, M Pechenizkiy arXiv preprint arXiv:2202.02643, 2022 | 104 | 2022 |
| E2-train: Training state-of-the-art cnns with over 80% energy savings Y Wang, Z Jiang, X Chen, P Xu, Y Zhao, Y Lin, Z Wang Advances in Neural Information Processing Systems 32, 2019 | 98 | 2019 |
| Earlybert: Efficient bert training via early-bird lottery tickets X Chen, Y Cheng, S Wang, Z Gan, Z Wang, J Liu arXiv preprint arXiv:2101.00063, 2020 | 88 | 2020 |
| Shiftaddnet: A hardware-inspired deep network H You, X Chen, Y Zhang, C Li, S Li, Z Liu, Z Wang, Y Lin Advances in Neural Information Processing Systems 33, 2771-2783, 2020 | 87 | 2020 |
| Federated dynamic sparse training: Computing less, communicating less, yet learning better S Bibikar, H Vikalo, Z Wang, X Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6080-6088, 2022 | 86 | 2022 |
| Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? X Ma, G Yuan, X Shen, T Chen, X Chen, X Chen, N Liu, M Qin, S Liu, ... Neural Information Processing Systems 2021, 2021 | 60 | 2021 |
| Deep ensembling with no overhead for either training or testing: The all-round blessings of dynamic sparsity S Liu, T Chen, Z Atashgahi, X Chen, G Sokar, E Mocanu, M Pechenizkiy, ... arXiv preprint arXiv:2106.14568, 2021 | 54 | 2021 |
| Smartexchange: Trading higher-cost memory storage/access for lower-cost computation Y Zhao, X Chen, Y Wang, C Li, H You, Y Fu, Y Xie, Z Wang, Y Lin 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 51 | 2020 |
| Learning a minimax optimizer: A pilot study J Shen, X Chen, H Heaton, T Chen, J Liu, W Yin, Z Wang International Conference on Learning Representations, 2020 | 30 | 2020 |
| The Elastic Lottery Ticket Hypothesis X Chen, Y Cheng, S Wang, Z Gan, J Liu, Z Wang Neural Information Processing Systems 2021, 2021 | 27 | 2021 |
| Safeguarded learned convex optimization H Heaton, X Chen, Z Wang, W Yin Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7848-7855, 2023 | 26 | 2023 |
| Hyperparameter Tuning is All You Need for LISTA X Chen, J Liu, Z Wang, W Yin Neural Information Processing Systems 2021, 2021 | 24 | 2021 |
Zhangyang (Atlas) WangXTX Markets & University of Texas at AustinVerified email at utexas.edu
