7 citations · 8 across the 9 of their papers we have counts for
9 papers
Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation
Armand Rousselot, Joran Wendebourg, Ullrich Köthe
The performance of machine learning models depends on discovering features in the data that are relevant to solving the given task while ignoring irrelevant variation. To check and…
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…
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
Paul Saegert, Ullrich Köthe
Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the pred…
Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions
Sander Hummerich, Tristan Bereau, Ullrich Köthe
By reducing resolution, coarse-grained models greatly accelerate molecular simulations, unlocking access to long-timescale phenomena, though at the expense of microscopic informati…
Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows
Peter Sorrenson, Lukas Lührs, Hans Olischläger +1
Variational Autoencoders (VAEs) are powerful generative models widely used for learning interpretable latent spaces, quantifying uncertainty, and compressing data for downstream ge…
TRADE: Transfer of Distributions between External Conditions with Normalizing Flows
Stefan Wahl, Armand Rousselot, Felix Draxler +2
Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature…
Sensitivity-Aware Amortized Bayesian Inference
Lasse Elsemüller, Hans Olischläger, Marvin Schmitt +3
Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. While theoretically appealing, they are overwhelmingly inefficient fo…
On the Convergence Rate of Gaussianization with Random Rotations
Felix Draxler, Lars Kühmichel, Armand Rousselot +3
Gaussianization is a simple generative model that can be trained without backpropagation. It has shown compelling performance on low dimensional data. As the dimension increases, h…
Finding Competence Regions in Domain Generalization
Jens Müller, Stefan T. Radev, Robert Schmier +3
We investigate a "learning to reject" framework to address the problem of silent failures in Domain Generalization (DG), where the test distribution differs from the training distr…