9 papers
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…
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.…
Can LLM Agents Identify Spoken Dialects like a Linguist?
Tobias Bystrich, Lukas Hamm, Maria Hassan +3
Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability…
More Agents Improve Math Problem Solving but Adversarial Robustness Gap Persists
Khashayar Alavi, Zhastay Yeltay, Lucie Flek +1
When LLM agents work together, they seem to be more powerful than a single LLM in mathematical question answering. However, are they also more robust to adversarial inputs? We inve…
Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents
Mohammad H. A. Monfared, Lucie Flek, Akbar Karimi
We propose an agentic data augmentation method for Aspect-Based Sentiment Analysis (ABSA) that uses iterative generation and verification to produce high quality synthetic training…