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 …
AD White - Living journal of computational molecular science, 2022 - ncbi.nlm.nih.gov
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 …
C Esteves, A Makadia… - Advances in Neural …, 2020 - proceedings.neurips.cc
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 …