Darts+: Improved differentiable architecture search with early stopping H Liang, S Zhang, J Sun, X He, W Huang, K Zhuang, Z Li arXiv preprint arXiv:1909.06035, 2019 | 337 | 2019 |
Boosting few-shot learning with adaptive margin loss A Li, W Huang, X Lan, J Feng, Z Li, L Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 215 | 2020 |
Few-shot learning with global class representations A Li, T Luo, T Xiang, W Huang, L Wang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 133 | 2019 |
Multi-round influence maximization L Sun, W Huang, PS Yu, W Chen Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 113 | 2018 |
Towards the Generalization of Contrastive Self-Supervised Learning W Huang, M Yi, X Zhao, Z Jiang The Eleventh International Conference on Learning Representations, 2022 | 84 | 2022 |
Locally differentially private (contextual) bandits learning K Zheng, T Cai, W Huang, Z Li, L Wang Advances in Neural Information Processing Systems 33, 12300-12310, 2020 | 54 | 2020 |
New interpretations of normalization methods in deep learning J Sun, X Cao, H Liang, W Huang, Z Chen, Z Li Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5875-5882, 2020 | 42 | 2020 |
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4 L Wei, Z Jiang, W Huang, L Sun arXiv preprint arXiv:2308.12067, 2023 | 26 | 2023 |
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding T Hu, Z Liu, F Zhou, W Wang, W Huang arXiv preprint arXiv:2205.14814, 2022 | 16 | 2022 |
Modeling local dependence in natural language with multi-channel recurrent neural networks C Xu, W Huang, H Wang, G Wang, TY Liu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5525-5532, 2019 | 16 | 2019 |
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations X Zhao, T Du, Y Wang, J Yao, W Huang arXiv preprint arXiv:2303.01092, 2023 | 15 | 2023 |
Combinatorial pure exploration with continuous and separable reward functions and its applications. W Huang, J Ok, L Li, W Chen IJCAI, 2291-2297, 2018 | 15* | 2018 |
ChemLLM: A Chemical Large Language Model D Zhang, W Liu, Q Tan, J Chen, H Yan, Y Yan, J Li, W Huang, X Yue, ... arXiv preprint arXiv:2402.06852, 2024 | 14 | 2024 |
Rethinking weak supervision in helping contrastive learning J Cui, W Huang, Y Wang, Y Wang International Conference on Machine Learning, 6448-6467, 2023 | 9 | 2023 |
Can pretext-based self-supervised learning be boosted by downstream data? a theoretical analysis J Teng, W Huang, H He International Conference on Artificial Intelligence and Statistics, 4198-4216, 2022 | 9 | 2022 |
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method M Zhang, X Zhao, J Yao, C Yuan, W Huang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 8* | 2023 |
Meta-learning PAC-Bayes priors in model averaging Y Huang, W Huang, L Li, Z Li Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4198-4205, 2020 | 8 | 2020 |
Matrix Information Theory for Self-Supervised Learning Y Zhang, Z Tan, J Yang, W Huang, Y Yuan arXiv preprint arXiv:2305.17326, 2023 | 7* | 2023 |
Information Flow in Self-Supervised Learning Z Tan, J Yang, W Huang, Y Yuan, Y Zhang arXiv preprint arXiv:2309.17281, 2023 | 6 | 2023 |
Community exploration: from offline optimization to online learning X Chen, W Huang, W Chen, J Lui Advances in Neural Information Processing Systems 31, 2018 | 6 | 2018 |