| Stein neural sampler T Hu, Z Chen, H Sun, J Bai, M Ye, G Cheng arXiv preprint arXiv:1810.03545, 2018 | 44 | 2018 |
| Regularization matters: A nonparametric perspective on overparametrized neural network T Hu, W Wang, C Lin, G Cheng International Conference on Artificial Intelligence and Statistics, 829-837, 2021 | 35 | 2021 |
| Sharp rate of convergence for deep neural network classifiers under the teacher-student setting T Hu, Z Shang, G Cheng arXiv preprint arXiv:2001.06892, 2020 | 21 | 2020 |
| Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding T Hu, Z Liu, F Zhou, W Wang, W Huang International Conference on Learning Representations 11, 2023 | 15 | 2023 |
| Understanding square loss in training overparametrized neural network classifiers T Hu, J Wang, W Wang, Z Li Advances in Neural Information Processing Systems 35, 16495-16508, 2022 | 15 | 2022 |
| Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models W Luo, T Hu, S Zhang, J Sun, Z Li, Z Zhang Advances in Neural Information Processing Systems 36, 76525-76546, 2023 | 13 | 2023 |
| ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning H Yang, L Hong, A Li, T Hu, Z Li, GH Lee, L Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 11 | 2023 |
| ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization Q Dong, A Muhammad, F Zhou, C Xie, T Hu, Y Yang, SH Bae, Z Li Advances in Neural Information Processing Systems 35, 31583-31598, 2022 | 11 | 2022 |
| Inducing neural collapse in deep long-tailed learning X Liu, J Zhang, T Hu, H Cao, Y Yao, L Pan International Conference on Artificial Intelligence and Statistics, 11534-11544, 2023 | 9 | 2023 |
| Explore and exploit the diverse knowledge in model zoo for domain generalization Y Chen, T Hu, F Zhou, Z Li, ZM Ma International Conference on Machine Learning, 4623-4640, 2023 | 8 | 2023 |
| ConsistentNeRF: Enhancing neural radiance fields with 3D consistency for sparse view synthesis S Hu, K Zhou, K Li, L Yu, L Hong, T Hu, Z Li, GH Lee, Z Liu arXiv preprint arXiv:2305.11031, 2023 | 8 | 2023 |
| Continual Learning by Modeling Intra-Class Variation L Yu, T Hu, L Hong, Z Liu, A Weller, W Liu Transactions on Machine Learning Research, 2023 | 8 | 2023 |
| Inter-rater reliability of web-based calibrated peer review within a pharmacy curriculum AN Isaacs, ML Miller, T Hu, B Johnson, ZA Weber American journal of pharmaceutical education 84 (4), 7583, 2020 | 7 | 2020 |
| Random smoothing regularization in kernel gradient descent learning L Ding, T Hu, J Jiang, D Li, W Wang, Y Yao arXiv preprint arXiv:2305.03531, 2023 | 6 | 2023 |
| Deciphering the projection head: Representation evaluation self-supervised learning J Ma, T Hu, W Wang arXiv preprint arXiv:2301.12189, 2023 | 5 | 2023 |
| Complexity Matters: Rethinking the Latent Space for Generative Modeling T Hu, F Chen, H Wang, J Li, W Wang, J Sun, Z Li Advances in Neural Information Processing Systems 36, 29558-29579, 2023 | 4 | 2023 |
| Boosting visual-language models by exploiting hard samples H Wang, M Huang, R Huang, L Hong, H Xu, T Hu, X Liang, Z Li arXiv preprint arXiv:2305.05208, 2023 | 4 | 2023 |
| Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification P Piwek, A Klukowski, T Hu Uncertainty in Artificial Intelligence, 1673--1683, 2023 | 3 | 2023 |
| Elucidating The Design Space of Classifier-Guided Diffusion Generation J Ma, T Hu, W Wang, J Sun International Conference on Learning Representations 12, 2024 | 2 | 2024 |
| Training Energy-Based Models with Diffusion Contrastive Divergences W Luo, H Jiang, T Hu, J Sun, Z Li, Z Zhang arXiv preprint arXiv:2307.01668, 2023 | 1 | 2023 |
Zhenguo LiHuawei Noah's Ark LabVerified email at huawei.com
