Publications

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2025

Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation (2025)

Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann

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2025

Regularized least squares learning with heavy-tailed noise is minimax optimal (2025)

Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier, Arthur Gretton

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2025

P03-08 Can automatic rodent behavior analysis using AI/ML contribute to drug safety? First insights from DeepRod (2025)

B. Weiss, K. Eschmann, C. Weinandi, P. Schwarz, F.-Z. Khamlichi, H. Behnke, M. Garafolj, O. Akhtar, A. Loy, H. Schauerte, T. Wollmann, G. Rast

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2025

Compressing Large Language Models to Any Size Without Re-Computation (2025)

Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann

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2025

Deep Joint Source-Channel Coding for Small Satellite Applications (2025)

Olga Kondrateva, Grace Li Zhang, Julian Zobel, Björn Scheuermann, Stefan Dietzel

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2024

Squirrel: A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way (2024)

Alireza Sohofi, Tiansu Yu, Alp Aribal, Winfried Loetzsch, Thomas Wollmann

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2024

Memorization with neural nets: going beyond the worst case (2024)

Sjoerd Dirksen, Patrick Finke, Martin Genzel

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2024

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms (2024)

Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li

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2024

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm (2024)

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

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2024

Adaptable Deep Joint Source-and-Channel Coding for Small Satellite Applications

Olga Kondrateva, Stefan Dietzel, Björn Scheuermann

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2024

Quantority: Parameter Prioritization for Incremental Updates of Convolutional Neural Networks in Small Satellite Missions (2024)

Olga Kondrateva; Stefan Dietzel; Björn Scheuermann

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2024

DeepRod: A human-in-the-loop system for automatic rodent behavior analysis (2024)

Adrian Loy, Miha Garafolj, Heike Schauerte, Hanna Behnke, Cyrille Charnier, Philipp Schwarz, Georg Rast, Thomas Wollmann

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2024

Progressive Updates of Convolutional Neural Networks for Enhanced Reliability in Small Satellite Applications (2024)

Olga Kondrateva, Stefan Dietzel, Maximilian Schambach, Johannes Otterbach, Björn Scheuermann

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2024

Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques (2024)

Lukas Baur, Konstantin Ditschuneit, Maximilian Schambach, Can Kaymakci, Thomas Wollmann, Alexander Sauer

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2024

Integrating Cloud Computing, Bayesian Optimization, and Neural-Additive Modeling for Enhanced CAM Systems in 5-Axis Milling (2024)

Viktor Rudel, Georg Vinogradov, Philipp Ganser, Thomas Bergs, Christopher Vahl, Markus Frings, Valentina König, Maximilian Schambach, Stefan Dietzel, Michael Königs

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2023

Multiscale Neural Operators for Solving Time-Independent PDEs (2023)

Winfried Ripken, Lisa Coiffard, Felix Pieper, Sebastian Dziadzio

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2023

Scaling Experiments in Self-Supervised Cross-Table Representation Learning (2023)

Maximilian Schambach, Dominique Paul, Johannes S. Otterbach

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2023

Towards Tabular Foundation Models - Status Quo, Challenges, and Opportunities (2023)

Maximilian Schambach

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2023

Self-distilled Representation Learning for Time Series (2023)

Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes Otterbach

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2023

Curve your Enthusiasm: Concurvity Regularization in Differentiable GAMs (2023)

Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes Otterbach, Martin Genzel

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2023

Joint Source-and-Channel Coding for Small Satellite Applications (2023)

Olga Kondrateva, Stefan Dietzel, Björn Scheuermann

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2023

SECREDAS: Safe and (Cyber-)Secure Cooperative and Automated Mobility (2023)

Sebastian Gerres

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2023

Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization (2023)

Olga Kondrateva, Stefan Dietzel, Maximilian Schambach, Johannes Otterbach, Björn Scheuermann,

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2023

Parameter Prioritization for Efficient Transmission of Neural Networks in Small Satellite Applications (2023)

Olga Kondrateva, Stefan Dietzel, Ansgar Lößer, Björn Scheuermann

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2023

Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation (2023).

Alexander Koenig, Maximilian Schambach, Johannes S. Otterbach

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2023

SECREDAS: Safe and (Cyber-) Secure Cooperative and Automated Mobility (2023)

Chris van der Ploeg, Jacco van de Sluis, Sebastian Gerres, Szabolcs Novaczki, András Wippelhauser, Eric Nassor, Julien Sevin, András Gazdag, Gergely Biczók

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2023

NAM-CAM: Neural-Additive Models for Semi-analytic Descriptions of CAM Simulations (2023)

Konstantin Ditschuneit, Adem Frenk, Markus Frings, Viktor Rudel, Stefan Dietzel, Johannes S. Otterbach

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2023

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management (2023).

Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach

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2022

Auto-Compressing Subset Pruning for Semantic Image Segmentation (2022)

Konstantin Ditschuneit, Johannes S. Otterbach

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2022

Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks (2022).

Simon Ohler, Daniel Steven Brady, Winfried Lötzsch, Michael Fleischhauer, Johannes Otterbach

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2022

Scalable Flow Optimization for Small Satellite Networks using Benders Decomposition (2022).

Olga Kondrateva, Stefan Dietzel, Björn Scheuermann

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2022

Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks (2022).

Winfried Lötzsch, Simon Ohler, Johannes S. Otterbach

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2021

Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs (2021)

Johannes Otterbach, Thomas Wollmann

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2021

DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows (2021).

Samuel von Baußnern, Johannes Otterbach, Adrian Loy, Mathieu Salzmann, Thomas Wollmann

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2021

MEAL: Manifold Embedding-based Active Learning (2021).

Deepthi Sreenivasaiah, Johannes Otterbach, Thomas Wollmann

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