272 citations · 289 across the 9 of their papers we have counts for
9 papers
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
Jialin Chen, Yuelin Wang, Cristian Bodnar +3
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistin…
Multi-level Protein Representation Learning for Blind Mutational Effect Prediction
Yang Tan, Bingxin Zhou, Yuanhong Jiang +2
Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions. Accurately predicting the effects…
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Bingxin Zhou, Outongyi Lv, Kai Yi +4
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications. While deep learning methods le…
Graph Representation Learning for Interactive Biomolecule Systems
Xinye Xiong, Bingxin Zhou, Yu Guang Wang
Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accuratel…
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Chenxin Xu, Robby T. Tan, Yuhong Tan +4
Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geomet…
Adaptive Importance Sampling and Quasi-Monte Carlo Methods for 6G URLLC Systems
Xiongwen Ke, Houying Zhu, Kai Yi +3
In this paper, we propose an efficient simulation method based on adaptive importance sampling, which can automatically find the optimal proposal within the Gaussian family based o…