Deep Clustering and Representation Learning that Preserves Geometric Structures. - Lirong Wu; Zicheng Liu 006; Zelin Zang; Jun Xia 001; Siyuan Li 002; Stan Z. Li (CoRR, 2020)

Co-learning: Learning from Noisy Labels with Self-supervision. - Cheng Tan 012; Jun Xia 001; Lirong Wu; Stan Z. Li (ACM Multimedia, 2021)

Invertible Manifold Learning for Dimension Reduction. - Siyuan Li 002; Haitao Lin; Zelin Zang; Lirong Wu; Jun Xia 001; Stan Z. Li (ECML/PKDD (3), 2021)

Debiased Graph Contrastive Learning. - Jun Xia 001; Lirong Wu; Jintao Chen; Ge Wang; Stan Z. Li (CoRR, 2021)

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings. - Jiangbin Zheng 002; Yile Wang 001; Ge Wang; Jun Xia 001; Yufei Huang 002; Guojiang Zhao; Yue Zhang 004; Stan Z. Li (ACL (1), 2022)

OT Cleaner: Label Correction as Optimal Transport. - Jun Xia 001; Cheng Tan 012; Lirong Wu; Yongjie Xu; Stan Z. Li (ICASSP, 2022)

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning. - Jun Xia 001; Lirong Wu; Ge Wang; Jintao Chen; Stan Z. Li (ICML, 2022)

GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction. - Lirong Wu; Jun Xia 001; Zhangyang Gao; Haitao Lin; Cheng Tan 012; Stan Z. Li (ECML/PKDD (4), 2022)

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation. - Lirong Wu; Zicheng Liu 006; Jun Xia 001; Zelin Zang; Siyuan Li 002; Stan Z. Li (WACV, 2022)

SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation. - Jun Xia 001; Lirong Wu; Jintao Chen; Bozhen Hu; Stan Z. Li (WWW, 2022)

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications. - Jun Xia 001; Yanqiao Zhu 001; Yuanqi Du; Stan Z. Li (CoRR, 2022)

Generative De Novo Protein Design with Global Context. - Cheng Tan 012; Zhangyang Gao; Jun Xia 001; Stan Z. Li (CoRR, 2022)

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification. - Lirong Wu; Jun Xia 001; Haitao Lin; Zhangyang Gao; Zicheng Liu 006; Guojiang Zhao; Stan Z. Li (CoRR, 2022)

A Systematic Survey of Molecular Pre-trained Models. - Jun Xia 001; Yanqiao Zhu 001; Yuanqi Du; Yue Liu 008; Stan Z. Li (CoRR, 2022)

A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application. - Yue Liu 008; Jun Xia 001; Sihang Zhou 001; Siwei Wang 001; Xifeng Guo 001; Xihong Yang; Ke Liang 006; Wenxuan Tu; Stan Z. Li; Xinwang Liu 002 (CoRR, 2022)

Protein Language Models and Structure Prediction: Connection and Progression. - Bozhen Hu; Jun Xia 001; Jiangbin Zheng 002; Cheng Tan 012; Yufei Huang 002; Yongjie Xu; Stan Z. Li (CoRR, 2022)

Multi-level disentanglement graph neural network. - Lirong Wu; Haitao Lin; Jun Xia 001; Cheng Tan 012; Stan Z. Li (Neural Comput. Appl., 2022)

Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning. - Cheng Tan 012; Zhangyang Gao; Lirong Wu; Yongjie Xu; Jun Xia 001; Siyuan Li 002; Stan Z. Li (CVPR, 2023)

CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment. - Jiangbin Zheng 002; Yile Wang 001; Cheng Tan 012; Siyuan Li 002; Ge Wang; Jun Xia 001; Yidong Chen 001; Stan Z. Li (CVPR, 2023)

Deep Manifold Graph Auto-Encoder For Attributed Graph Embedding. - Bozhen Hu; Zelin Zang; Jun Xia 001; Lirong Wu; Cheng Tan 012; Stan Z. Li (ICASSP, 2023)

Global-Context Aware Generative Protein Design. - Cheng Tan 012; Zhangyang Gao; Jun Xia 001; Bozhen Hu; Stan Z. Li (ICASSP, 2023)

Wordreg: Mitigating the Gap between Training and Inference with Worst-Case Drop Regularization. - Jun Xia 001; Ge Wang; Bozhen Hu; Cheng Tan 012; Jiangbin Zheng 002; Yongjie Xu; Stan Z. Li (ICASSP, 2023)

Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules. - Jun Xia 001; Chengshuai Zhao; Bozhen Hu; Zhangyang Gao; Cheng Tan 012; Yue Liu 008; Siyuan Li 002; Stan Z. Li (ICLR, 2023)

Dink-Net: Neural Clustering on Large Graphs. - Yue Liu 008; Ke Liang 006; Jun Xia 001; Sihang Zhou 001; Xihong Yang; Xinwang Liu 002; Stan Z. Li (ICML, 2023)

A Systematic Survey of Chemical Pre-trained Models. - Jun Xia 001; Yanqiao Zhu 001; Yuanqi Du; Stan Z. Li (IJCAI, 2023)

Reinforcement Graph Clustering with Unknown Cluster Number. - Yue Liu 008; Ke Liang 006; Jun Xia 001; Xihong Yang; Sihang Zhou 001; Meng Liu 014; Xinwang Liu 002; Stan Z. Li (ACM Multimedia, 2023)

CONVERT: Contrastive Graph Clustering with Reliable Augmentation. - Xihong Yang; Cheng Tan 012; Yue Liu 008; Ke Liang 006; Siwei Wang 001; Sihang Zhou 001; Jun Xia 001; Stan Z. Li; Xinwang Liu 002; En Zhu (ACM Multimedia, 2023)

Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions. - Jun Xia 001; Lecheng Zhang; Xiao Zhu; Yue Liu 008; Zhangyang Gao; Bozhen Hu; Cheng Tan 012; Jiangbin Zheng 002; Siyuan Li 002; Stan Z. Li (NeurIPS, 2023)

Co-supervised Pre-training of Pocket and Ligand. - Zhangyang Gao; Cheng Tan 012; Jun Xia 001; Stan Z. Li (ECML/PKDD (1), 2023)

Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction? - Jun Xia 001; Lecheng Zhang; Xiao Zhu; Stan Z. Li (CoRR, 2023)

Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View. - Cheng Tan 012; Jue Wang 004; Zhangyang Gao; Siyuan Li 002; Lirong Wu; Jun Xia 001; Stan Z. Li (CoRR, 2023)

MMDesign: Multi-Modality Transfer Learning for Generative Protein Design. - Jiangbin Zheng 002; Siyuan Li 002; Yufei Huang 002; Zhangyang Gao; Cheng Tan 012; Bozhen Hu; Jun Xia 001; Ge Wang; Stan Z. Li (CoRR, 2023)

Cross-Gate MLP with Protein Complex Invariant Embedding Is a One-Shot Antibody Designer. - Cheng Tan 012; Zhangyang Gao; Lirong Wu; Jun Xia 001; Jiangbin Zheng 002; Xihong Yang; Yue Liu 008; Bozhen Hu; Stan Z. Li (AAAI, 2024)

DiscoGNN: A Sample-Efficient Framework for Self-Supervised Graph Representation Learning. - Jun Xia 001; Shaorong Chen; Yue Liu 008; Zhangyang Gao; Jiangbin Zheng 002; Xihong Yang; Stan Z. Li (ICDE, 2024)

KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement. - Zhangyang Gao; Cheng Tan 012; Xingran Chen; Yijie Zhang; Jun Xia 001; Siyuan Li 002; Stan Z. Li (ICLR, 2024)

Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective. - Cheng Tan 012; Zhangyang Gao; Hanqun Cao; Xingran Chen; Ge Wang; Lirong Wu; Jun Xia 001; Jiangbin Zheng 002; Stan Z. Li (ICML, 2024)

A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer. - Zhangyang Gao; Daize Dong; Cheng Tan 012; Jun Xia 001; Bozhen Hu; Stan Z. Li (ICML, 2024)

MMGNN: A Molecular Merged Graph Neural Network for Explainable Solvation Free Energy Prediction. - Wenjie Du 003; Shuai Zhang; Di Wu 057; Jun Xia 001; Ziyuan Zhao; Junfeng Fang; Yang Wang 015 (IJCAI, 2024)

AdaNovo: Towards Robust emph{De Novo} Peptide Sequencing in Proteomics against Data Biases. - Jun Xia 001; Shaorong Chen; Jingbo Zhou; Xiaojun Shan; Wenjie Du 003; Zhangyang Gao; Cheng Tan 012; Bozhen Hu; Jiangbin Zheng 002; Stan Z. Li (NeurIPS, 2024)

Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure. - Bozhen Hu; Cheng Tan 012; Jun Xia 001; Yue Liu 008; Lirong Wu; Jiangbin Zheng 002; Yongjie Xu; Yufei Huang 002; Stan Z. Li (NeurIPS, 2024)

ProtGO: Function-Guided Protein Modeling for Unified Representation Learning. - Bozhen Hu; Cheng Tan 012; Yongjie Xu; Zhangyang Gao; Jun Xia 001; Lirong Wu; Stan Z. Li (NeurIPS, 2024)

FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning. - Sizhe Liu; Jun Xia 001; Lecheng Zhang; Yuchen Liu; Yue Liu 008; Wenjie Du 003; Zhangyang Gao; Bozhen Hu; Cheng Tan 012; Hongxin Xiang; Stan Z. Li (NeurIPS, 2024)

NovoBench: Benchmarking Deep Learning-based emph{De Novo} Sequencing Methods in Proteomics. - Jingbo Zhou; Shaorong Chen; Jun Xia 001; Sizhe Liu; Tianze Ling; Wenjie Du 003; Yue Liu 008; Jianwei Yin; Stan Z. Li (NeurIPS, 2024)

Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module. - Jingbo Zhou; Yixuan Du; Ruqiong Zhang; Jun Xia 001; Zhizhi Yu; Zelin Zang; Di Jin 001; Carl Yang 001; Rui Zhang; Stan Z. Li (NeurIPS, 2024)

Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation. - Yongjie Xu; Zelin Zang; Bozhen Hu; Yue Yuan; Cheng Tan 012; Jun Xia 001; Stan Z. Li (Briefings Bioinform., 2024)

Masked Modeling for Self-supervised Representation Learning on Vision and Beyond. - Siyuan Li 002; Luyuan Zhang; Zedong Wang; Di Wu 057; Lirong Wu; Zicheng Liu 006; Jun Xia 001; Cheng Tan 012; Yang Liu 356; Baigui Sun; Stan Z. Li (CoRR, 2024)

Graph-level Protein Representation Learning by Structure Knowledge Refinement. - Ge Wang; Zelin Zang; Jiangbin Zheng 002; Jun Xia 001; Stan Z. Li (CoRR, 2024)

AdaNovo: Adaptive emph{De Novo} Peptide Sequencing with Conditional Mutual Information. - Jun Xia 001; Shaorong Chen; Jingbo Zhou; Tianze Ling; Wenjie Du 003; Sizhe Liu; Stan Z. Li (CoRR, 2024)

NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics. - Jingbo Zhou; Shaorong Chen; Jun Xia 001; Sizhe Liu; Tianze Ling; Wenjie Du 003; Yue Liu 008; Jianwei Yin; Stan Z. Li (CoRR, 2024)

GNN Cleaner: Label Cleaner for Graph Structured Data. - Jun Xia 001; Haitao Lin; Yongjie Xu; Cheng Tan 012; Lirong Wu; Siyuan Li 002; Stan Z. Li (IEEE Trans. Knowl. Data Eng., 2024)

MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction. - Cheng Tan 012; Zhenxiao Cao; Zhangyang Gao; Lirong Wu; Siyuan Li 002; Yufei Huang 002; Jun Xia 001; Bozhen Hu; Stan Z. Li (ICLR, 2025)

ReNovo: Retrieval-Based emph{De Novo} Mass Spectrometry Peptide Sequencing. - Shaorong Chen; Jun Xia 001; Jingbo Zhou; Lecheng Zhang; Zhangyang Gao; Bozhen Hu; Cheng Tan 012; Wenjie Du 003; Stan Z. Li (ICLR, 2025)

Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo. - Jun Xia 001; Sizhe Liu; Jingbo Zhou; Shaorong Chen; Hongxin Xiang; Zicheng Liu 006; Yue Liu 008; Stan Z. Li (ICLR, 2025)

GuardReasoner: Towards Reasoning-based LLM Safeguards. - Yue Liu 008; Hongcheng Gao; Shengfang Zhai; Jun Xia 001; Tianyi Wu; Zhiwei Xue; Yulin Chen; Kenji Kawaguchi; Jiaheng Zhang; Bryan Hooi (CoRR, 2025)

Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks. - Yuang Jia; Xiaojuan Shan; Jun Xia 001; Guancheng Wan; Yuchen Zhang; Wenke Huang; Mang Ye; Stan Z. Li (CoRR, 2025)

GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype. - Changxi Chi; Jun Xia 001; Jingbo Zhou; Jiabei Cheng; Chang Yu 001; Stan Z. Li (CoRR, 2025)

Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges. - Changxi Chi; Jun Xia 001; Yufei Huang 002; Jingbo Zhou; Siyuan Li 002; Yunfan Liu 002; Chang Yu 001; Stan Z. Li (CoRR, 2025)