Search Results for author:
Found 7 papers, 5 papers with code
Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow
This paper presents a method for finding a sparse representation of Barron functions.
Resolution-Invariant Image Classification based on Fourier Neural Operators
In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs).
Uniform Convergence Rates for Lipschitz Learning on Graphs
In this work we prove uniform convergence rates for solutions of the graph infinity Laplace equation as the number of vertices grows to infinity.
Neural Architecture Search via Bregman Iterations
We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations.
A Bregman Learning Framework for Sparse Neural Networks
In contrast to established methods for sparse training the proposed family of algorithms constitutes a regrowth strategy for neural networks that is solely optimization-based without additional heuristics.
Ranked #164 on
Image Classification
on CIFAR-10
CLIP: Cheap Lipschitz Training of Neural Networks
Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability.
Continuum Limit of Lipschitz Learning on Graphs
In particular, we define a sequence of functionals which approximate the largest local Lipschitz constant of a graph function and prove $\Gamma$-convergence in the $L^\infty$-topology to the supremum norm of the gradient as the graph becomes denser.
