Although deep neural networks have achieved super-human performance on m...
In this paper we prove Gamma-convergence of a nonlocal perimeter of Mink...
In this paper we propose polarized consensus-based dynamics in order to ...
In this paper we prove the first quantitative convergence rates for the ...
We establish an equivalence between a family of adversarial training pro...
Lipschitz learning is a graph-based semi-supervised learning method wher...
We propose a novel strategy for Neural Architecture Search (NAS) based o...
This chapter describes how gradient flows and nonlinear power methods in...
We propose a learning framework based on stochastic Bregman iterations t...
The Ensemble Kalman inversion (EKI), proposed by Iglesias et al. for the...
Despite the large success of deep neural networks (DNN) in recent years,...
The susceptibility of deep neural networks to untrustworthy predictions,...
Tackling semi-supervised learning problems with graph-based methods have...
We study variational regularisation methods for inverse problems with
im...
In this work we present an alternative formulation of the higher eigenva...
Multi-modality (or multi-channel) imaging is becoming increasingly impor...
In this work we investigate the computation of nonlinear eigenfunctions ...
Hyperspectral imaging is a cutting-edge type of remote sensing used for
...