arXiv:2604. 23716v2 Announce Type: replace Abstract: Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross-entropy is the default classification loss, mutual information underpins representation learning and feature selection, and transfer entropy reveals directed influence in dynamical systems.
By Nikolaos Al. Papadopoulos, Konstantinos E. Psannis
arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.
By Shalaleh Rismani, Roel Dobbe, AJung Moon
arXiv:2607. 15196v1 Announce Type: cross Abstract: We present a novel viewpoint for uncertainty quantification.
By Raghad Alamri, Michele Caprio, Gavin Brown
arXiv:2607. 27710v1 Announce Type: new Abstract: Mutual information is a general measure of statistical dependence that captures both linear and nonlinear relationships between random variables.
By Petra Eerikinharju, Marko Tuononen, Ville Hautam\"aki
arXiv:2601. 06077v2 Announce Type: replace-cross Abstract: This work aims to rigorously define the values of perception, prediction, communication, and common sense in decision making.
By Aolin Xu
arXiv:2606. 04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction.
By Vasileios Sevetlidis