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Shuiwang Ji, Professor and Truchard Family Chair
Shuiwang Ji, Professor and Truchard Family Chair
Department of Computer Science & Engineering, Texas A&M University
Подтвержден адрес электронной почты в домене tamu.edu - Главная страница
Название
Процитировано
Процитировано
Год
3D convolutional neural networks for human action recognition
S Ji, W Xu, M Yang, K Yu
IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1), 221-231, 2013
77032013
Graph U-Nets
H Gao, S Ji
The 36th International Conference on Machine Learning, 2083-2092, 2019
15142019
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
W Zhang, R Li, H Deng, L Wang, W Lin, S Ji, D Shen
NeuroImage 108, 214-224, 2015
9782015
SLEP: Sparse learning with efficient projections
J Liu, S Ji, J Ye
Arizona State University 6 (491), 7, 2009
831*2009
Explainability in graph neural networks: A taxonomic survey
H Yuan, H Yu, S Gui, S Ji
IEEE transactions on pattern analysis and machine intelligence 45 (5), 5782-5799, 2022
8242022
Multi-task feature learning via efficient l2, 1-norm minimization
J Liu, S Ji, J Ye
arXiv preprint arXiv:1205.2631, 2012
8172012
Large-scale learnable graph convolutional networks
H Gao, Z Wang, S Ji
Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018
7312018
Towards deeper graph neural networks
M Liu, H Gao, S Ji
Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020
6912020
Deep learning based imaging data completion for improved brain disease diagnosis
R Li, W Zhang, HI Suk, L Wang, J Li, D Shen, S Ji
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014
6122014
An accelerated gradient method for trace norm minimization
S Ji, J Ye
Proceedings of the 26th annual international conference on machine learning …, 2009
5512009
On explainability of graph neural networks via subgraph explorations
H Yuan, H Yu, J Wang, K Li, S Ji
International conference on machine learning, 12241-12252, 2021
4712021
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), 2412-2429, 2022
4582022
Xgnn: Towards model-level explanations of graph neural networks
H Yuan, J Tang, X Hu, S Ji
Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020
4442020
A robust deep model for improved classification of AD/MCI patients
F Li, L Tran, KH Thung, S Ji, D Shen, J Li
IEEE journal of biomedical and health informatics 19 (5), 1610-1616, 2015
3512015
Spherical message passing for 3d molecular graphs
Y Liu, L Wang, M Liu, Y Lin, X Zhang, B Oztekin, S Ji
International Conference on Learning Representations (ICLR), 2022
346*2022
Feature selection based on structured sparsity: A comprehensive study
J Gui, Z Sun, S Ji, D Tao, T Tan
IEEE transactions on neural networks and learning systems 28 (7), 1490-1507, 2016
3402016
Position: TrustLLM: Trustworthiness in large language models
Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li, C Gao, Y Huang, W Lyu, ...
International Conference on Machine Learning, 20166-20270, 2024
338*2024
Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis
L Sun, S Ji, J Ye
IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (1), 194-200, 2010
3382010
Hypergraph spectral learning for multi-label classification
L Sun, S Ji, J Ye
Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008
3012008
Discriminant sparse neighborhood preserving embedding for face recognition
J Gui, Z Sun, W Jia, R Hu, Y Lei, S Ji
Pattern Recognition 45 (8), 2884-2893, 2012
3002012
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