From the 1 of 9 linked papers with an AI index.
9 papers
Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings
Laura Paul, Holger Rauhut, Martin Burger +2
The paper presents a hybrid method that treats crack detection in digitized paintings as an inverse problem, using a deep generative model to represent the crack-free artwork and a…
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
Simon Welker, Lorenz Kuger, Tim Roith +4
In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions…
Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers
Ahmad Aloradi, Tim Roith, Emanuël A. P. Habets +1
Sparse training reduces the memory and computational costs of deep neural networks. However, sparse optimization methods, e.g., those adding an penalty, often control spar…
Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime
Albert Alcalde, Leon Bungert, Konstantin Riedl +1
Transformers with self-attention modules as their core components have become an integral architecture in modern large language and foundation models. In this paper, we study the e…
Introduction to Regularization and Learning Methods for Inverse Problems
Danielle Bednarski, Tim Roith
These lecture notes evolve around mathematical concepts arising in inverse problems. We start by introducing inverse problems through examples such as differentiation, deconvolutio…
Adversarial flows: A gradient flow characterization of adversarial attacks
Lukas Weigand, Tim Roith, Martin Burger
A popular method to perform adversarial attacks on neuronal networks is the so-called fast gradient sign method and its iterative variant. In this paper, we interpret this method a…