This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …

We survey the mathematical foundations of geometric deep learning, focusing on group
equivariant and gauge equivariant neural networks. We develop gauge equivariant …

Embodied agents operate in a structured world, often solving tasks with spatial, temporal,
and permutation symmetries. Most algorithms for planning and model-based reinforcement …

Deep learning is becoming a standard tool in chemistry and materials science. Although
there are learning materials available for deep learning, none cover the applications in …

Learning equivariant representations is a promising way to reduce sample and model
complexity and improve the generalization performance of deep neural networks. The …

Deep neural networks can solve many kinds of learning problems, but only if a lot of data is
available. For many problems (eg in medical imaging), it is expensive to acquire a large …

Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …

We analyze the role of rotational equivariance in convolutional neural networks (CNNs)
applied to spherical images. We compare the performance of the group equivariant …