In this paper we introduce and study the algebraic generalization of non commutative
convolutional neural networks. We leverage the theory of algebraic signal processing to …

The quest for robust and generalizable machine learning models has driven recent interest
in exploiting symmetries through equivariant neural networks. In the context of PDE solvers …

In this paper, we develop a generalized theory of convolutional signal processing and
neural networks for Reproducing Kernel Hilbert Spaces (RKHS). Leveraging the theory of …

In this paper we discuss the results recently published in [1] about algebraic signal models
(ASMs) based on non commutative algebras and their use in convolutional neural networks …

Scene text image super-resolution aims to provide high-resolution and readable text images
to support scene text recognition. Although existing methods based on deep learning have …