Search Results for author: Shurui Gui

Found 9 papers, 7 papers with code

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

2 code implementations7 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.

Active Learning Learning Theory +1

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization

no code implementations13 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.

Data Augmentation Out-of-Distribution Generalization

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

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.

Out-of-Distribution Generalization

FlowX: Towards Explainable Graph Neural Networks via Message Flows

2 code implementations26 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.


GOOD: A Graph Out-of-Distribution Benchmark

1 code implementation16 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.

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

1 code implementation23 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.

Benchmarking Graph Generation +1

Explainability in Graph Neural Networks: A Taxonomic Survey

no code implementations31 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.

FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation

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.

Optical Flow Estimation Texture Synthesis +1

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