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cs.LG2026
Rethinking Intrinsic Dimension Estimation in Neural Representations
Rickmer Schulte, David Rügamer
The analysis of neural representation has become an integral part of research aiming to better understand the inner workings of neural networks. While there are many different appr…
cs.LG2026
Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
Emanuel Sommer, Rickmer Schulte, Sarah Deubner +2
Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles (DEs) with flexible multi-c…
cs.LG2025
Additive Model Boosting: New Insights and Path(ologie)s
Rickmer Schulte, David Rügamer
Additive models (AMs) have sparked a lot of interest in machine learning recently, allowing the incorporation of interpretable structures into a wide range of model classes. Many c…