Publications (5)
Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning
Timo Saala, Matthias Schott
Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage t…
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…
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…
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations
Timo Saala, Lucie Flek, Alexander Jung +5
Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high…
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.…