3 papers
cs.AI2026
Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios
Julius Wirbel, P. Nicholas Hansen, Line K. H. Clemmensen +1
Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different domains, most notable in the au…
cs.CV2026
Post-hoc Self-explanation of CNNs
Ahcène Boubekki, Line H. Clemmensen
Although standard Convolutional Neural Networks (CNNs) can be mathematically reinterpreted as Self-Explainable Models (SEMs), their built-in prototypes do not on their own accurate…
cs.LG2024
A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection
Sebastián Basterrech, Line Clemmensen, Gerardo Rubino
Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a m…