Graph neural networks inspired by classical iterative algorithms Y Yang, T Liu, Y Wang, J Zhou, Q Gan, Z Wei, Z Zhang, Z Huang, D Wipf International Conference on Machine Learning, 11773-11783, 2021 | 70 | 2021 |
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks H Ahn, Y Yang, Q Gan, T Moon, DP Wipf Advances in Neural Information Processing Systems 35, 38436-38448, 2022 | 25 | 2022 |
Transformers from an Optimization Perspective Y Yang, Z Huang, D Wipf arXiv preprint arXiv:2205.13891, 2022 | 22 | 2022 |
Relation of the relations: A new paradigm of the relation extraction problem Z Jin, Y Yang, X Qiu, Z Zhang arXiv preprint arXiv:2006.03719, 2020 | 14 | 2020 |
Implicit vs unfolded graph neural networks Y Yang, T Liu, Y Wang, Z Huang, D Wipf arXiv preprint arXiv:2111.06592, 2021 | 12 | 2021 |
Going beyond linear mode connectivity: The layerwise linear feature connectivity Z Zhou, Y Yang, X Yang, J Yan, W Hu Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
Why Propagate Alone? Parallel Use of Labels and Features on Graphs Y Wang, J Jin, W Zhang, Y Yang, J Chen, Q Gan, Y Yu, Z Zhang, Z Huang, ... arXiv preprint arXiv:2110.07190, 2021 | 7 | 2021 |
Are neurons actually collapsed? on the fine-grained structure in neural representations Y Yang, J Steinhardt, W Hu International Conference on Machine Learning, 39453-39487, 2023 | 6 | 2023 |
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks Y Yang, J Yang, W Hu, M Dereziński arXiv preprint arXiv:2403.18142, 2024 | | 2024 |