Lirong Wu

发表

Invertible Manifold Learning for Dimension Reduction pdf

Stan Z. Li, Haitao Lin, Siyuan Li, 2020, ECML/PKDD.

STONet: A Neural-Operator-Driven Spatio-temporal Network

Stan Z. Li, Lirong Wu, Haitao Lin, 2022, ArXiv.

SimVP: Simpler yet Better Video Prediction

Stan Z. Li, Lirong Wu, Cheng Tan, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

Stan Z. Li, Q. Zhang, Dragomir Radev, 2022, ArXiv.

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

Stan Z. Li, Stan Z.Li, Lirong Wu, 2021, ICML.

CoSP: Co-supervised pretraining of pocket and ligand

Stan Z. Li, Lirong Wu, Cheng Tan, 2022, ArXiv.

Hyperspherical Consistency Regularization

Stan Z. Li, Siyuan Li, Lirong Wu, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

RETRACTED: Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization

Ziqing Li, Z. Zang, Lirong Wu, 2022, International journal of molecular sciences.

Exploring Generative Neural Temporal Point Process

Stan Z. Li, Haitao Lin, Lirong Wu, 2022, Trans. Mach. Learn. Res..

Decoupled Mixup for Data-efficient Learning

Stan Z. Li, Zicheng Liu, Siyuan Li, 2022, ArXiv.

Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation

Stan Z. Li, Yufei Huang, Zicheng Liu, 2022, ArXiv.

Phenotype Classification using Proteome Data in a Data-Independent Acquisition Tensor Format.

Stan Z. Li, N. Iyer, Zhongzhi Luan, 2020, Journal of the American Society for Mass Spectrometry.

Are Gradients on Graph Structure Reliable in Gray-box Attacks?

Zicheng Liu, Stan Z. Li, Zicheng Liu, 2022, CIKM.

AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

Stan Z. Li, Zicheng Liu, Di Wu, 2021, ECCV.

Non-equispaced Fourier Neural Solvers for PDEs

Siyuan Li, Yufei Huang, Haitao Lin, 2022, ArXiv.

A Survey on Protein Representation Learning: Retrospect and Prospect

Stan Z. Li, Yu-Feng Huang, Lirong Wu, 2022, ArXiv.

Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

Dragomir R. Radev, Stan Z. Li, Siyuan Li, 2022, 2205.07266.

Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

Dragomir R. Radev, Stan Z. Li, Q. Zhang, 2022, ArXiv.

Multi-level disentanglement graph neural network

Stan Z. Li, Haitao Lin, Lirong Wu, 2022, Neural Computing and Applications.

Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy

Stan Z. Li, Yufei Huang, Jiangbin Zheng, 2023, ArXiv.

Git: Clustering Based on Graph of Intensity Topology pdf

Lirong Wu, Stan Z. Li, Haitao Lin, 2021, ArXiv.

SemiRetro: Semi-template framework boosts deep retrosynthesis prediction

Stan Z. Li, Lirong Wu, Cheng Tan, 2022, ArXiv.

Deep Clustering and Visualization for End-to-End High-Dimensional Data Analysis

Stan Z. Li, Lirong Wu, Guojiang Zhao, 2022, IEEE Transactions on Neural Networks and Learning Systems.

GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction

Stan Z. Li, Haitao Lin, Lirong Wu, 2022, ECML/PKDD.

Explaining Graph Neural Networks via Non-parametric Subgraph Matching

Dragomir R. Radev, Stan Z. Li, Siyuan Li, 2023, ArXiv.

Multi-level disentanglement graph neural network

Stan Z. Li, Lirong Wu, Jun Xia, 2022, Neural Computing and Applications.

Self-supervised on Graphs: Contrastive, Generative, or Predictive

Stan Z. Li, Lirong Wu, Cheng Tan, 2021, ArXiv.

SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

Stan Z. Li, Lirong Wu, Jun Xia, 2022, WWW.

Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting

Lirong Wu, Stan Z. Li, Haitao Lin, 2021, AAAI.

Deep Manifold Transformation for Dimension Reduction.

S. Li, Z. Zang, Lirong Wu, 2020 .

Co-learning: Learning from Noisy Labels with Self-supervision

Lirong Wu, Stan Z. Li, Cheng Tan, 2021, ACM Multimedia.

AutoMix: Unveiling the Power of Mixup pdf

Siyuan Li, Lirong Wu, Zicheng Liu, 2021, ArXiv.

Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias

Stan Z. Li, Lirong Wu, Yun Luo, 2023, NeurIPS.

GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction pdf

Lirong Wu, Zhangyang Gao, Stan Z. Li, 2021, ArXiv.

Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive

Lirong Wu, Haitao Lin, Cheng Tan, 2021, IEEE Transactions on Knowledge and Data Engineering.

Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

Stan Z. Li, Yufei Huang, Haitao Lin, 2023, ICML.

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

Stan Z. Li, Zicheng Liu, Haitao Lin, 2022, ArXiv.

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Stan Z. Li, Siyuan Li, Lirong Wu, 2023, ArXiv.

Non-equispaced Fourier Neural Solvers for PDEs

Yufei Huang, Haitao Lin, Siyuan Li, 2022, ArXiv.

Deep Manifold Computing and Visualization pdf

Stan Z. Li, Zelin Zang, Lirong Wu, 2020, ArXiv.

OT Cleaner: Label Correction as Optimal Transport

Stan Z. Li, Lirong Wu, Jun Xia, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Debiased Graph Contrastive Learning pdf

Lirong Wu, Stan Z. Li, Ge Wang, 2021, ArXiv.

Markov-Lipschitz Deep Learning

S. Li, Zelin Zhang, Lirong Wu, 2020, ArXiv.

Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry

Xiao Yi, Oi Lian Kon, Zhongzhi Luan, 2020, bioRxiv.

Conditional Local Filters with Explainers for Spatio-Temporal Forecasting pdf

Haitao Lin, Lirong Wu, Zhangyang Gao, 2021, ArXiv.

Unveiling the Power of Mixup for Stronger Classiﬁers

Stan Z. Li, Di Wu, Siyuan Li, 2021 .

Consistent Representation Learning for High Dimensional Data Analysis pdf

Stan Z. Li, Zelin Zang, Lirong Wu, 2020, ArXiv.

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation

Stan Z. Li, Siyuan Li, Z. Zang, 2020, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Deep Clustering and Representation Learning with Geometric Structure Preservation

S. Li, Zicheng Liu, Siyuan Li, 2020 .

Integration of pre-trained protein language models into geometric deep learning networks

Dragomir R. Radev, Stan Z. Li, Jinbo Xu, 2023, Communications biology.

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

Stan Z. Li, Yufei Huang, Yue Liu, 2023, bioRxiv.

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration

Stan Z. Li, Yufei Huang, Haitao Lin, 2023, ArXiv.

Learning from noisy labels with self-supervision

Stan Z. Li, Lirong Wu, Jun Xia, 2021 .

Masked Modeling for Self-supervised Representation Learning on Vision and Beyond

Siyuan Li, Baigui Sun, Siyuan Li, 2023, ArXiv.

An Empirical Study: Extensive Deep Temporal Point Process

Lirong Wu, Stan Z. Li, Haitao Lin, 2021, 2110.09823.

Extensive Deep Temporal Point Process pdf

Lirong Wu, Stan Z. Li, Haitao Lin, 2021, ArXiv.

Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness

Zelin Zang, Stan Z. Li, Lirong Wu, 2020 .

A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation

Lirong Wu, Guojiang Zhao, Zhangyang Gao, 2024, IEEE Transactions on Knowledge and Data Engineering.

Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks

Lirong Wu, Cheng Tan, Zhangyang Gao, 2024, 2403.01400.

Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks

Stan Z. Li, Yufei Huang, Haitao Lin, 2022, NeurIPS.

Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions

Lirong Wu, Stan Z. Li, Haitao Lin, 2024, ArXiv.

MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding

N. Chawla, Siyuan Li, Lirong Wu, 2024, ArXiv.

Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge

Siyuan Li, Lirong Wu, Cheng Tan, 2024, ArXiv.

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Stan Z. Li, Siyuan Li, Lirong Wu, 2023, NeurIPS.

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration

Stan Z. Li, Yufei Huang, Haitao Lin, 2023, NeurIPS.

Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework

Stan Z. Li, Yufei Huang, Haitao Lin, 2023, AAAI.

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction

Lirong Wu, Cheng Tan, Zhangyang Gao, 2024, AAAI.

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Siyuan Li, Lirong Wu, Zhangyang Gao, 2023, AAAI.

ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics

Lirong Wu, Cheng Tan, Zhangyang Gao, 2023, NeurIPS.

Deep Clustering and Representation Learning that Preserves Geometric Structures

S. Li, Zicheng Liu, Siyuan Li, 2020, ArXiv.

Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

Dragomir R. Radev, Qiang Zhang, Stan Z. Li, 2022, ArXiv.

Advances of Deep Learning in Protein Science: A Comprehensive Survey

Lirong Wu, Jun Xia, Cheng Tan, 2024, 2403.05314.

GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation

Lirong Wu, Haitao Lin, Yufei Huang, 2024, bioRxiv.

Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching

Dragomir R. Radev, Stan Z. Li, Siyuan Li, 2023, ICML.

Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View

Siyuan Li, Lirong Wu, Jun Xia, 2023, ArXiv.

GNN Cleaner: Label Cleaner for Graph Structured Data

Stan Z. Li, Haitao Lin, Siyuan Li, 2024, IEEE Transactions on Knowledge and Data Engineering.

Invertible Manifold Learning for Dimension Reduction

Stan Z. Li, Haitao Lin, Siyuan Li, 2020, ECML/PKDD.

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

Siyuan Li, Lirong Wu, Guojiang Zhao, 2024, ArXiv.

Effectiveness and efficiency: label-aware hierarchical subgraph learning for protein-protein interaction

Lirong Wu, Haitao Lin, Yufei Huang, 2024, bioRxiv.