Zobrazeno 1 - 10
of 249
pro vyhledávání: '"Holler, Martin"'
Autor:
Viti, Bruno, Thaler, Franz, Kapper, Kathrin Lisa, Urschler, Martin, Holler, Martin, Karabelas, Elias
Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an
Externí odkaz:
http://arxiv.org/abs/2411.06911
Autor:
Holler, Martin, Morina, Erion
This paper addresses the problem of uniqueness in learning physical laws for systems of partial differential equations (PDEs). Contrary to most existing approaches, it considers a framework of structured model learning, where existing, approximately
Externí odkaz:
http://arxiv.org/abs/2410.22009
Autor:
Morina, Erion, Holler, Martin
This work focuses on the analysis of fully connected feed forward ReLU neural networks as they approximate a given, smooth function. In contrast to conventionally studied universal approximation properties under increasing architectures, e.g., in ter
Externí odkaz:
http://arxiv.org/abs/2406.14936
Autor:
Habring, Andreas, Holler, Martin
This review provides an introduction to - and overview of - the current state of the art in neural-network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a
Externí odkaz:
http://arxiv.org/abs/2312.14849
This paper is concerned with sampling from probability distributions $\pi$ on $\mathbb{R}^d$ admitting a density of the form $\pi(x) \propto e^{-U(x)}$, where $U(x)=F(x)+G(Kx)$ with $K$ being a linear operator and $G$ being non-differentiable. Two di
Externí odkaz:
http://arxiv.org/abs/2308.01417
This work is concerned with the identifiability of metabolic parameters from multi-region measurement data in quantitative PET imaging. It shows that, for the frequently used two-tissue compartment model and under reasonable assumptions, it is possib
Externí odkaz:
http://arxiv.org/abs/2305.16989
Autor:
Bredies, Kristian, Carioni, Marcello, Holler, Martin, Korolev, Yury, Schönlieb, Carola-Bibiane
In this paper we introduce the class of infinite infimal convolution functionals and apply these functionals to the regularization of ill-posed inverse problems. The proposed regularization involves an infimal convolution of a continuously parametriz
Externí odkaz:
http://arxiv.org/abs/2304.08628
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the posterior variance together with techniques from conformal prediction in o
Externí odkaz:
http://arxiv.org/abs/2212.12499