no code implementations • 16 Nov 2023 • Gabriele Cesa, Arash Behboodi
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.
no code implementations • 14 Nov 2023 • Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi
Lattice reduction is a combinatorial optimization problem aimed at finding the most orthogonal basis in a given lattice.
1 code implementation • NeurIPS 2023 • Maksim Zhdanov, Nico Hoffmann, Gabriele Cesa
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.
no code implementations • 24 Oct 2022 • Arash Behboodi, Gabriele Cesa, Taco Cohen
Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model.
no code implementations • ICLR 2022 • Gabriele Cesa, Leon Lang, Maurice Weiler
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.
no code implementations • 21 Apr 2020 • Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen, Max Welling
Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community.
1 code implementation • NeurIPS 2019 • Maurice Weiler, Gabriele Cesa
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
7 code implementations • 19 Nov 2019 • Maurice Weiler, Gabriele Cesa
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