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cs.LG2026
Geometric and Information Compression of Representations in Deep Learning
Linara Adilova, Henning Petzka, Asja Fischer +1
Deep neural networks transform input data into latent representations that support a wide range of downstream tasks. These representations can be characterized along information-th…
cs.LG2025
On the Role of Priors in Bayesian Causal Learning
Bernhard C. Geiger, Roman Kern
In this work, we investigate causal learning of independent causal mechanisms from a Bayesian perspective. Confirming previous claims from the literature, we show in a didactically…
cs.LG2024
Constraining Anomaly Detection with Anomaly-Free Regions
Maximilian Toller, Hussain Hussain, Roman Kern +1
We propose the novel concept of anomaly-free regions (AFR) to improve anomaly detection. An AFR is a region in the data space for which it is known that there are no anomalies insi…