Martin Genzel — accepted AI-conference papers

2026

Float8@2bits: Entropy Coding Enables Data-Free Model Compression

Patrick Putzky, Martin Genzel, Mattes Mollenhauer, Sebastian Schulze, Thomas Wollmann, Stefan Dietzel

ICML 2026poster

Post-training compression is currently divided into two contrasting regimes. On the one hand, fast, data-free, and model-agnostic methods (e.g., NF4 or HQQ) offer maximum accessibility but suffer from functional collapse at extreme bit-rates below 4 bits. On the other hand, techniques leveraging cal…

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2023

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models

Julien Niklas Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes Otterbach +1

NeurIPS 2023poster

Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the current enthusiasm for GAMs, their susceptibility to concurvity — i…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗

2022

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

ICML 2022oral

This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy? Positive evidence is provided for the first time, focusing on a prototypical computed tomography (CT) setup. We d…

Cited by 39 Source ↗PDF ↗code ↗Scholar ↗