48 citations · 288 across the 47 of their papers we have counts for
20 papers · 1 filter
VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention
Jingbo Zhou, Jun Xia, Siyuan Li +11
Graph Transformer has demonstrated impressive capabilities in the field of graph representation learning. However, existing approaches face two critical challenges: (1) most models…
Multimodal Regression for Enzyme Turnover Rates Prediction
Bozhen Hu, Cheng Tan, Siyuan Li +4
The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most org…
AlphaFold Database Debiasing for Robust Inverse Folding
Cheng Tan, Zhenxiao Cao, Zhangyang Gao +3
The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein…
MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction
Cheng Tan, Zhenxiao Cao, Zhangyang Gao +6
Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biol…
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…
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…
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…
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
Lirong Wu, Yijun Tian, Yufei Huang +4
Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities. The growing demand and cost of experimental PPI assays r…
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Siyuan Li, Zicheng Liu, Juanxi Tian +9
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DN…
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Yufei Huang, Siyuan Li, Jin Su +9
Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimati…
SemiReward: A General Reward Model for Semi-supervised Learning
Siyuan Li, Weiyang Jin, Zedong Wang +4
Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish…
Behavior Contrastive Learning for Unsupervised Skill Discovery
Rushuai Yang, Chenjia Bai, Hongyi Guo +5
In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual informatio…
Instructor-inspired Machine Learning for Robust Molecular Property Prediction
Fang Wu, Shuting Jin, Siyuan Li +1
Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical da…
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Fang Wu, Siyuan Li, Xurui Jin +4
The success of graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant of the prediction?'' Particularl…
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
Yiqin Yang, Hao Hu, Wenzhe Li +4
Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios…
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning
Cheng Tan, Zhangyang Gao, Siyuan Li +1
Recent years have witnessed remarkable advances in spatiotemporal predictive learning, with methods incorporating auxiliary inputs, complex neural architectures, and sophisticated…
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…
Offline Reinforcement Learning with Reverse Model-based Imagination
Jianhao Wang, Wenzhe Li, Haozhe Jiang +3
In offline reinforcement learning (offline RL), one of the main challenges is to deal with the distributional shift between the learning policy and the given dataset. To address th…
Active Hierarchical Exploration with Stable Subgoal Representation Learning
Siyuan Li, Jin Zhang, Jianhao Wang +2
Goal-conditioned hierarchical reinforcement learning (GCHRL) provides a promising approach to solving long-horizon tasks. Recently, its success has been extended to more general se…
Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation
Lirong Wu, Zicheng Liu, Zelin Zang +3
Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss…