3 papers
cs.CL2026
Stochasticity in Tokenisation Improves Robustness
Sophie Steger, Rui Li, Sofiane Ennadir +4
The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation of the input indicate that m…
cs.LG2025
Accelerated Execution of Bayesian Neural Networks using a Single Probabilistic Forward Pass and Code Generation
Bernhard Klein, Falk Selker, Hendrik Borras +3
Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in s…
cs.LG2024
Robustness of Explainable Artificial Intelligence in Industrial Process Modelling
Benedikt Kantz, Clemens Staudinger, Christoph Feilmayr +4
eXplainable Artificial Intelligence (XAI) aims at providing understandable explanations of black box models. In this paper, we evaluate current XAI methods by scoring them based on…