2 papers
cs.LG2026
From Core to Detail: Unsupervised Disentanglement with Entropy-Ordered Flows
Daniel Galperin, Ullrich Köthe
The unsupervised discovery of features that are both semantically meaningful and stable across runs remains a central challenge in representation learning. We introduce entropy-ord…
cs.LG2024
Analyzing Generative Models by Manifold Entropic Metrics
Daniel Galperin, Ullrich Köthe
Good generative models should not only synthesize high quality data, but also utilize interpretable representations that aid human understanding of their behavior. However, it is d…