13 citations · 52 across the 22 of their papers we have counts for
13 papers · 1 filter
FlowTS: Time Series Generation via Rectified Flow
Yang Hu, Xiao Wang, Zezhen Ding +7
Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical s…
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
Lirong Wu, Haitao Lin, Guojiang Zhao +2
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on message passing to perform fea…
A Survey on Mixup Augmentations and Beyond
Xin Jin, Hongyu Zhu, Siyuan Li +6
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive la…
Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation
Lirong Wu, Yunfan Liu, Haitao Lin +2
To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences
Zicheng Liu, Siyuan Li, Li Wang +3
To mitigate the computational complexity in the self-attention mechanism on long sequences, linear attention utilizes computation tricks to achieve linear complexity, while state s…
GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
Zicheng Liu, Jiahui Li, Siyuan Li +5
The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application…
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
Siyuan Li, Zedong Wang, Zicheng Liu +5
Similar to natural language models, pre-trained genome language models are proposed to capture the underlying intricacies within genomes with unsupervised sequence modeling. They h…
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
Haitao Lin, Guojiang Zhao, Odin Zhang +7
Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. How…
Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
Lirong Wu, Yijun Tian, Haitao Lin +4
Protein-protein bindings play a key role in a variety of fundamental biological processes, and thus predicting the effects of amino acid mutations on protein-protein binding is cru…
PPFlow: Target-aware Peptide Design with Torsional Flow Matching
Haitao Lin, Odin Zhang, Huifeng Zhao +5
Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored.…
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory
Zicheng Liu, Li Wang, Siyuan Li +3
Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences. Alth…
FoldToken: Learning Protein Language via Vector Quantization and Beyond
Zhangyang Gao, Cheng Tan, Jue Wang +3
Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to…