E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products

NeurIPSSpotlight2025

Authors
Yunyang Li, Lin Huang, Zhihao Ding, Xinran Wei, Chu Wang, Han Yang, Zun Wang, Chang Liu, Yu Shi, Peiran Jin, Tao Qin, Mark Gerstein, Jia Zhang
Venue
NeurIPS 2025
Track
Spotlight

TL;DR

Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science…

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Topics

graph neural network transformer efficient biology graph

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