| 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 | 82* | 2022 |
| On the neural tangent kernel of deep networks with orthogonal initialization W Huang, W Du, RY Da Xu IJCAI 2021, 2020 | 39 | 2020 |
| Deep Active Learning by Leveraging Training Dynamics H Wang, W Huang, A Margenot, H Tong, J He NeurIPS 2022, 2021 | 35 | 2021 |
| 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 | 35 | 2021 |
| Auto-scaling Vision Transformers without Training W Chen, W Huang, X Du, X Song, Z Wang, D Zhou ICLR 2022, 2022 | 33 | 2022 |
| Understanding and improving feature learning for out-of-distribution generalization Y Chen*, W Huang*, K Zhou*, Y Bian, B Han, J Cheng NeurIPS 2023, 2024 | 32 | 2024 |
| On the Equivalence between Neural Network and Support Vector Machine Y Chen, W Huang, LM Nguyen, TW Weng NeurIPS 2021, 2021 | 31 | 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 | 27 | 2024 |
| 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 | 25 | 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 | 18 | 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 | 18 | 2016 |
| Global and Local Prompts Cooperation via Optimal Transport for Federated Learning H Li, W Huang, J Wang, Y Shi CVPR 2024, 2024 | 14 | 2024 |
| Graph Lottery Ticket Automated G Zhang, K Wang, W Huang, Y Yue, Y Wang, R Zimmermann, A Zhou, ... ICLR 2024, 2023 | 13 | 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 | 13 | 2023 |
| Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory W Huang, Y Shi, Z Cai, T Suzuki ICLR 2024, 2023 | 11 | 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 | 11* | 2023 |
| 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 |
| 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 | 10* | 2020 |
| DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition S Guan, X Yu, W Huang, G Fang, H Lu IEEE Transactions on Image Processing, 2023 | 9 | 2023 |
| Weighted Mutual Learning with Diversity-Driven Model Compression M Zhang, L Wang, DGC Campos, W Huang, C Guo, B Yang NeurIPS 2022, 2022 | 9 | 2022 |
Prof Richard Yi Da XuHong Kong Baptist UniversityVerified email at hkbu.edu.hk