| Air pollution in China: Status and spatiotemporal variations C Song*, L Wu*, Y Xie*, J He, X Chen, T Wang, Y Lin, T Jin, A Wang, Y Liu, ... Environmental pollution 227, 334-347, 2017 | 619 | 2017 |
| Self-supervised learning of graph neural networks: A unified review Y Xie, Z Xu, J Zhang, Z Wang, S Ji IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 2412 …, 2022 | 446 | 2022 |
| DIG: A turnkey library for diving into graph deep learning research M Liu*, Y Luo*, L Wang*, Y Xie*, H Yuan*, S Gui, H Yu, Z Xu, J Zhang, ... Journal of Machine Learning Research 22 (240), 1-9, 2021 | 146* | 2021 |
| Advanced graph and sequence neural networks for molecular property prediction and drug discovery Z Wang*, M Liu*, Y Luo*, Z Xu*, Y Xie*, L Wang*, L Cai*, Q Qi, Z Yuan, ... Bioinformatics 38 (9), 2579-2586, 2022 | 123 | 2022 |
| Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 116 | 2023 |
| Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising Y Xie, Z Wang, S Ji Advances in Neural Information Processing Systems 33, 20320-20330, 2020 | 111 | 2020 |
| Numerical model-based artificial neural network model and its application for quantifying impact factors of urban air quality J He, Y Yu, Y Xie, H Mao, L Wu, N Liu, S Zhao Water, Air, & Soil Pollution 227, 1-16, 2016 | 65 | 2016 |
| Global voxel transformer networks for augmented microscopy Z Wang*, Y Xie*, S Ji Nature Machine Intelligence 3 (2), 161-171, 2021 | 41 | 2021 |
| Molecule3d: A benchmark for predicting 3d geometries from molecular graphs Z Xu, Y Luo, X Zhang, X Xu, Y Xie, M Liu, K Dickerson, C Deng, M Nakata, ... arXiv preprint arXiv:2110.01717, 2021 | 39 | 2021 |
| Self-Supervised Representation Learning via Latent Graph Prediction Y Xie*, Z Xu*, S Ji International Conference on Machine Learning, 24460-24477, 2022 | 38 | 2022 |
| Task-Agnostic Graph Explanations Y Xie, S Katariya, X Tang, E Huang, N Rao, K Subbian, S Ji Neural Information Processing Systems, 2022 | 31 | 2022 |
| Group contrastive self-supervised learning on graphs X Xu, C Deng, Y Xie, S Ji IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 3169-3180, 2022 | 25 | 2022 |
| Finding the stars in the fireworks: Deep understanding of motion sensor fingerprint XY Li, H Liu, L Zhang, Z Wu, Y Xie, G Chen, C Wan, Z Liang IEEE/ACM Transactions on Networking 27 (5), 1945-1958, 2019 | 21 | 2019 |
| Fast quantum property prediction via deeper 2d and 3d graph networks M Liu, C Fu, X Zhang, L Wang, Y Xie, H Yuan, Y Luo, Z Xu, S Xu, S Ji arXiv preprint arXiv:2106.08551, 2021 | 13 | 2021 |
| SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations X Zhang, J Helwig, Y Lin, Y Xie, C Fu, S Wojtowytsch, S Ji arXiv preprint arXiv:2403.19507, 2024 | 8 | 2024 |
| Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging K Patel, Z Xie, H Yuan, SMS Islam, Y Xie, W He, W Zhang, A Gottlieb, ... Communications Biology 7 (1), 414, 2024 | 7 | 2024 |
| A mathematical view of attention models in deep learning S Ji, Y Xie, H Gao Texas A&M University, April, 2019 | 7 | 2019 |
| 3D Molecular Geometry Analysis with 2D Graphs Z Xu, Y Xie, Y Luo, X Zhang, X Xu, M Liu, K Dickerson, C Deng, M Nakata, ... Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | 3 | 2024 |
| Simrag: Self-improving retrieval-augmented generation for adapting large language models to specialized domains R Xu, H Liu, S Nag, Z Dai, Y Xie, X Tang, C Luo, Y Li, JC Ho, C Yang, ... arXiv preprint arXiv:2410.17952, 2024 | 2 | 2024 |
| Augmented Equivariant Attention Networks for Microscopy Image Transformation Y Xie, Y Ding, S Ji IEEE Transactions on Medical Imaging, 2022 | 2* | 2022 |
Shuiwang Ji, Professor and Truchard...Department of Computer Science & Engineering, Texas A&M UniversityVerified email at tamu.edu