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Found 8 papers, 3 papers with code
Algebraic Topological Networks via the Persistent Local Homology Sheaf
The intermediate features of our networks live in these vector spaces and we leverage the associated sheaf Laplacian to construct more complex linear messages between them.
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
Lattice reduction is a combinatorial optimization problem aimed at finding the most orthogonal basis in a given lattice.
Implicit Convolutional Kernels for Steerable CNNs
Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations.
A PAC-Bayesian Generalization Bound for Equivariant Networks
Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model.
A Program to Build E(N)-Equivariant Steerable CNNs
This enables us to directly parameterize filters in terms of a band-limited basis on the base space, but also to easily implement steerable CNNs equivariant to a large number of groups.
A Data and Compute Efficient Design for Limited-Resources Deep Learning
Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community.
General E(2)-Equivariant Steerable CNNs
Here we give a general description of E(2)-equivariant convolutions in the framework of Steerable CNNs.
Ranked #2 on
Rotated MNIST
on Rotated MNIST
General $E(2)$-Equivariant Steerable CNNs
Here we give a general description of $E(2)$-equivariant convolutions in the framework of Steerable CNNs.
Ranked #35 on
Image Classification
on STL-10
