Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Learning to predict agent motions with relationship reasoning is important for many
applications. In motion prediction tasks, maintaining motion equivariance under Euclidean …

This paper introduces a generative model equivariant to Euclidean symmetries: E (n)
Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E (n) …

A bstract Deep learning methods have been increasingly adopted to study jets in particle
physics. Since symmetry-preserving behavior has been shown to be an important factor for …

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the
state-of-the-art neural network models are approaching ab initio accuracy for molecular …

Quantum chemical (QC) property prediction is crucial for computational materials and drug
design, but relies on expensive electronic structure calculations like density functional theory …

Abstract Group equivariance (eg SE (3) equivariance) is a critical physical symmetry in
science, from classical and quantum physics to computational biology. It enables robust and …

Pretraining molecular representation models without labels is fundamental to various
applications. Conventional methods mainly process 2D molecular graphs and focus solely …

Equivariant neural networks, whose hidden features transform according to representations
of a group $ G $ acting on the data, exhibit training efficiency and an improved …