294 citations · 737 across the 61 of their papers we have counts for
16 papers · 1 filter
A Survey on Protein Representation Learning: Retrospect and Prospect
Lirong Wu, Yufei Huang, Haitao Lin +1
Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physi…
Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
Cheng Tan, Zhangyang Gao, Hanqun Cao +6
The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although dee…
Non-equispaced Fourier Neural Solvers for PDEs
Haitao Lin, Lirong Wu, Yongjie Xu +4
Solving partial differential equations is difficult. Recently proposed neural resolution-invariant models, despite their effectiveness and efficiency, usually require equispaced sp…
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Haitao Lin, Yufei Huang, Odin Zhang +7
Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where e…
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
Lirong Wu, Yufei Huang, Haitao Lin +3
Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning. With hundreds of self-supervised pretext tasks proposed over the past…
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Lirong Wu, Jun Xia, Haitao Lin +4
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) re…
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
Zihan Liu, Yun Luo, Lirong Wu +3
Graph edge perturbations are dedicated to damaging the prediction of graph neural networks by modifying the graph structure. Previous gray-box attackers employ gradients from the s…
Exploring Generative Neural Temporal Point Process
Haitao Lin, Lirong Wu, Guojiang Zhao +2
Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historica…
CoSP: Co-supervised pretraining of pocket and ligand
Zhangyang Gao, Cheng Tan, Lirong Wu +1
Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space? Pretraining molecules and proteins has attracted considerab…
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu +1
From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training str…
Hyperspherical Consistency Regularization
Cheng Tan, Zhangyang Gao, Lirong Wu +2
Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning…
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning
Cheng Tan, Zhangyang Gao, Lirong Wu +4
Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework…
STONet: A Neural-Operator-Driven Spatio-temporal Network
Haitao Lin, Guojiang Zhao, Lirong Wu +1
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great exp…
Harnessing Hard Mixed Samples with Decoupled Regularizer
Zicheng Liu, Siyuan Li, Ge Wang +3
Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup met…
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction
Zhangyang Gao, Cheng Tan, Lirong Wu +1
Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded…
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Jun Xia, Lirong Wu, Jintao Chen +2
Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations tha…