2 code implementations • 7 Apr 2024 • Shurui Gui, Xiner Li, Shuiwang Ji
Extensive experimental results confirm consistency with our theoretical analyses and show that the proposed ATTA method yields substantial performance improvements over TTA methods while maintaining efficiency and shares similar effectiveness to the more demanding active domain adaptation (ADA) methods.
1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
no code implementations • 13 Jun 2023 • Xiner Li, Shurui Gui, Youzhi Luo, Shuiwang Ji
Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution.
2 code implementations • NeurIPS 2023 • Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji
In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs.
2 code implementations • 26 Jun 2022 • Shurui Gui, Hao Yuan, Jie Wang, Qicheng Lao, Kang Li, Shuiwang Ji
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms.
1 code implementation • 16 Jun 2022 • Shurui Gui, Xiner Li, Limei Wang, Shuiwang Ji
Our GOOD benchmark is a growing project and expects to expand in both quantity and variety of resources as the area develops.
1 code implementation • 23 Mar 2021 • Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
no code implementations • 31 Dec 2020 • Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji
To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability.
1 code implementation • CVPR 2020 • Shurui Gui, Chaoyue Wang, Qihua Chen, Dacheng Tao
In the first stage, deep structure-aware features are employed to predict feature flows from two consecutive frames to their intermediate result, and further generate the structure image of the intermediate frame.
Ranked #15 on Video Frame Interpolation on X4K1000FPS