Wei Huang

According to our database, Wei Huang authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs.

Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Graph Lottery Ticket Automated.

Guibin Zhang, Kun Wang, Wei Huang, Yanwei Yue, Yang Wang, Roger Zimmermann, Aojun Zhou, Dawei Cheng, Jin Zeng, Yuxuan Liang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model.

Hao Wu, Yuxuan Liang, Wei Xiong, Zhengyang Zhou, Wei Huang, Shilong Wang, Kun Wang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection.

Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng

Trans. Mach. Learn. Res., 2023

Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model.

Hao Wu, Shilong Wang, Yuxuan Liang, Zhengyang Zhou, Wei Huang, Wei Xiong, Kun Wang

CoRR, 2023

Analyzing Generalization of Neural Networks through Loss Path Kernels.

Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Pruning graph neural networks by evaluating edge properties.

Li Wang, Wei Huang, Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Weidong Su

Knowl. Based Syst., 2022

Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning.

Wei Huang, Chunrui Liu, Yilan Chen, Tianyu Liu, Richard Yi Da Xu

CoRR, 2022

Weighted Mutual Learning with Diversity-Driven Model Compression.

Miao Zhang, Li Wang, David Campos, Wei Huang, Chenjuan Guo, Bin Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations.

Miao Zhang, Wei Huang, Bin Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective.

Wei Huang, Yayong Li, Weitao Du, Richard Y. D. Xu, Jie Yin, Ling Chen, Miao Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Understanding deep learning through ultra-wide neural networks

Wei Huang

PhD thesis, 2021

Gaussian process latent variable model factorization for context-aware recommender systems.

Wei Huang, Richard Yi Da Xu

Pattern Recognit. Lett., 2021

Differentiable Architecture Search Without Training Nor Labels: A Pruning Perspective.

Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Wei Huang, Gholamreza Haffari

CoRR, 2021

Wide Graph Neural Networks: Aggregation Provably Leads to Exponentially Trainability Loss.

Wei Huang, Yayong Li, Weitao Du, Richard Yi Da Xu, Jie Yin, Ling Chen

CoRR, 2021

On the Equivalence between Neural Network and Support Vector Machine.

Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization.

Wei Huang, Weitao Du, Richard Yi Da Xu

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

Implicit bias of deep linear networks in the large learning rate phase.

Wei Huang, Weitao Du, Richard Yi Da Xu, Chunrui Liu

CoRR, 2020

Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply.

Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020