This image shows David Holzmüller

David Holzmüller

M. Sc.

Research Assistant
Institute for Stochastics and Applications
Chair for Stochastik


Pfaffenwaldring 57
70569 Stuttgart
Room: 8.552


Research focus:

training behavior of neural networks and their possible applications in a simulation context.

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Viktor Zaverkin, David Holzmüller, Ingo Steinwart, and Johannes Kästner, Exploring chemical and conformational spaces by batch mode deep active learning, Digital Discovery, 2022.

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart, A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022

Viktor Zaverkin, David Holzmüller, Robin Schuldt, and Johannes Kästner, Predicting properties of periodic systems from cluster data: A case study of liquid water, J. Chem. Phys. 156, 114103, 2022

David Holzmüller and Dirk Pflüger, Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework, 2021. In: Bungartz, HJ., Garcke, J., Pflüger, D. (eds) Sparse Grids and Applications - Munich 2018. Lecture Notes in Computational Science and Engineering, vol 144. Springer, Cham.

V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments, J. Chem. Theory Comput. 17, 6658–6670, 2021

David Holzmüller, On the Universality of the Double Descent Peak in Ridgeless Regression, 2020

Daniel F. B. Haeufle, Isabell Wochner, David Holzmüller, Danny Driess, Michael Günther, Syn Schmitt, Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking, 2020

David Holzmüller, Ingo Steinwart, Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, 2020

David Holzmüller, Improved Approximation Schemes for the Restricted Shortest Path Problem, 2017 (

David Holzmüller, Efficient Neighbor-Finding on Space-Filling Curves, 2017 (

Zum Porträt des Monats am Fachbereich, Februar 2020

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