Towards understanding the data dependency of mixup-style training M Chidambaram, X Wang, Y Hu, C Wu, R Ge arXiv preprint arXiv:2110.07647, 2021 | 30 | 2021 |
Dissecting hessian: Understanding common structure of hessian in neural networks Y Wu, X Zhu, C Wu, A Wang, R Ge arXiv preprint arXiv:2010.04261, 2020 | 24 | 2020 |
Guarantees for tuning the step size using a learning-to-learn approach X Wang, S Yuan, C Wu, R Ge International Conference on Machine Learning, 10981-10990, 2021 | 22 | 2021 |
Rong Ge. Dissecting hessian: Understanding common structure of hessian in neural networks Y Wu, X Zhu, C Wu, A Wang arXiv preprint arXiv:2010.04261 2, 2020 | 18 | 2020 |
No spurious local minima in a two hidden unit relu network C Wu, J Luo, JD Lee | 14 | 2018 |
Beyond lazy training for over-parameterized tensor decomposition X Wang, C Wu, JD Lee, T Ma, R Ge Advances in Neural Information Processing Systems 33, 21934-21944, 2020 | 13 | 2020 |
Provably learning diverse features in multi-view data with midpoint mixup M Chidambaram, X Wang, C Wu, R Ge International Conference on Machine Learning, 5563-5599, 2023 | 7 | 2023 |
Secure data sharing with flow model C Wu, C Du, Y Yuan arXiv preprint arXiv:2009.11762, 2020 | 4 | 2020 |
Hiding data helps: on the benefits of masking for sparse coding M Chidambaram, C Wu, Y Cheng, R Ge International Conference on Machine Learning, 5600-5615, 2023 | 1 | 2023 |