| Single-pass contrastive learning can work for both homophilic and heterophilic graph H Wang, J Zhang, Q Zhu, W Huang*, K Kawaguchi, X Xiao TMLR 2023, 2022 | 83* | 2022 |
| On the neural tangent kernel of deep networks with orthogonal initialization W Huang, W Du, RY Da Xu IJCAI 2021, 2020 | 41 | 2020 |
| Understanding and improving feature learning for out-of-distribution generalization Y Chen, W Huang, K Zhou, Y Bian, B Han, J Cheng NeurIPS 2023 36, 68221-68275, 2023 | 40 | 2023 |
| Deep Active Learning by Leveraging Training Dynamics H Wang, W Huang, A Margenot, H Tong, J He NeurIPS 2022, 2021 | 37 | 2021 |
| Auto-scaling Vision Transformers without Training W Chen, W Huang, X Du, X Song, Z Wang, D Zhou ICLR 2022, 2022 | 34 | 2022 |
| Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective W Huang, Y Li, W Du, RY Da Xu, J Yin, L Chen, M Zhang ICLR 2022, 2021 | 34 | 2021 |
| Earthfarsser: Versatile spatio-temporal dynamical systems modeling in one model H Wu, Y Liang, W Xiong, Z Zhou, W Huang, S Wang, K Wang AAAI 2024, 2024 | 30 | 2024 |
| On the Equivalence between Neural Network and Support Vector Machine Y Chen, W Huang, LM Nguyen, TW Weng NeurIPS 2021, 2021 | 30 | 2021 |
| Critical percolation clusters in seven dimensions and on a complete graph W Huang, P Hou, J Wang, RM Ziff, Y Deng Physical Review E 97 (2), 022107, 2018 | 27 | 2018 |
| Pruning graph neural networks by evaluating edge properties L Wang, W Huang, M Zhang, S Pan, X Chang, SW Su Knowledge-Based Systems 256, 109847, 2022 | 19 | 2022 |
| Adaptive multi-GPU exchange Monte Carlo for the 3D random field Ising model CA Navarro, W Huang, Y Deng Computer Physics Communications 205, 48-60, 2016 | 17 | 2016 |
| Global and local prompts cooperation via optimal transport for federated learning H Li, W Huang, J Wang, Y Shi CVPR 2024, 12151-12161, 2024 | 15 | 2024 |
| Graph Lottery Ticket Automated G Zhang, K Wang, W Huang, Y Yue, Y Wang, R Zimmermann, A Zhou, ... ICLR 2024, 2023 | 15 | 2023 |
| Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective W Huang, Y Cao, H Wang, X Cao, T Suzuki ICML 2023 HiLD Workshop (Oral), 2023 | 14 | 2023 |
| Understanding convergence and generalization in federated learning through feature learning theory W Huang, Y Shi, Z Cai, T Suzuki The Twelfth International Conference on Learning Representations, 2023 | 14 | 2023 |
| Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection W Huang, C Liu, Y Chen, RY Da Xu, M Zhang, TW Weng Transactions on Machine Learning Research, 2023 | 12* | 2023 |
| Implicit bias of deep linear networks in the large learning rate phase W Huang, W Du, RY Da Xu, C Liu arXiv preprint arXiv:2011.12547, 2020 | 11* | 2020 |
| SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining A Han, J Li, W Huang, M Hong, A Takeda, P Jawanpuria, B Mishra NeurIPS 2024, 2024 | 10 | 2024 |
| Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis W Chen, W Huang, X Gong, B Hanin, Z Wang NeurIPS 2022, 2022 | 10 | 2022 |
| The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs K Wang, G Zhang, X Zhang, J Fang, X Wu, G Li, S Pan, W Huang*, ... KDD 2024, 2024 | 9 | 2024 |
Prof Richard Yi Da XuHong Kong Baptist UniversityPatvirtintas el. paštas hkbu.edu.hk
