1 citations · 1 across the 4 of their papers we have counts for
4 papers
Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Lucie Flek, Philipp Alexander Jungs, Akbar Karimi +6
Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedente…
Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications
Philip Bechtle, Lucie Flek, Philipp Alexander Jung +7
In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena.…
MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks
Lucie Flek, Oliver Janik, Philipp Alexander Jung +8
In this paper, we present a new algorithm, MiniFool, that implements physics-inspired adversarial attacks for testing neural network-based classification tasks in particle and astr…
Simulation of dielectric axion haloscopes with deep neural networks: a proof-of-principle
Philipp Alexander Jung, Bernardo Ary dos Santos, Dominik Bergermann +6
Dielectric axion haloscopes, such as the MADMAX experiment, are promising concepts for the direct search for dark matter axions. A reliable simulation is a fundamental requirement…