2 citations · 8 across the 6 of their papers we have counts for
6 papers
Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biol…
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He +3
Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the properties and simulate the behaviors of molecular systems. The molecular model is govern…
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
Bohang Zhang, Jingchu Gai, Yiheng Du +3
Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph learning community. So far, GNN expressiveness has been primarily assessed via the Weisfeiler-…
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Ruichen Li, Haotian Ye, Du Jiang +8
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of exis…
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
Bohang Zhang, Guhao Feng, Yiheng Du +2
Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs). While numerous architectures have been proposed, so far there…
3D Molecular Generation via Virtual Dynamics
Shuqi Lu, Lin Yao, Xi Chen +3
Structure-based drug design, i.e., finding molecules with high affinities to the target protein pocket, is one of the most critical tasks in drug discovery. Traditional solutions,…