12 citations · 28 across the 12 of their papers we have counts for
Showing 2024 · cs.LGShow all
2 papers · 2 filters
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…
cs.LG2024★ 2 cited
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe +1
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation pr…