4 papers · 1 filter
Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics
Matthias Schott, Lucie Flek
Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large…
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…
Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
Matthias Schott, Lucie Flek
Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data. In practice, experim…
In-Training Defenses against Emergent Misalignment in Language Models
David Kaczér, Magnus Jørgenvåg, Clemens Vetter +4
Fine-tuning lets practitioners repurpose aligned large language models (LLMs) for new domains, yet recent work reveals emergent misalignment (EM): Even a small, domain-specific fin…