Symmetry is one of the most central concepts in physics, and it is no surprise that it has also
been widely adopted as an inductive bias for machine-learning models applied to the …

Generating novel crystalline materials has potential to lead to advancements in fields such
as electronics, energy storage, and catalysis. The defining characteristic of crystals is their …

Equivariant neural networks (ENNs) have been shown to be extremely useful in many
applications involving some underlying symmetries. However, equivariant networks are …

Symmetries (transformations by group actions) are present in many datasets, and leveraging
them holds significant promise for improving predictions in machine learning. In this work …

Equivariance builds known symmetries into neural networks, often improving generalization.
However, equivariant networks cannot break self-symmetries present in any given input …

Generative modeling of symmetric densities has a range of applications in AI for science,
from drug discovery to physics simulations. The existing generative modeling paradigm for …

This work introduces a novel approach to achieving architecture-agnostic equivariance in
deep learning, particularly addressing the limitations of traditional equivariant architectures …

A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to
be permuted without altering the underlying problem structure. Recently, GNNs have …

Introducing Group Equivariant Convolution (GConv) empowers models to explore
symmetries hidden in visual data, improving their performance. However, in real-world …

Group Equivariant Convolution (GConv) can effectively handle rotational symmetry data.
They assume uniform and strict rotational symmetry across all features, as the …