This image shows David Holzmüller

David Holzmüller

M. Sc.

Research Assistant
Institute for Stochastics and Applications
Chair for Stochastik

Contact

Pfaffenwaldring 57
70569 Stuttgart
Germany
Room: 8.552

Subject

Research focus: Understanding and improving Deep Learning, using Deep Learning to improve simulations.

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

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 doi.org

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. doi.org

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 arxiv.org

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

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 https://www.frontiersin.org/articles/10.3389/frobt.2020.00077/full

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

David Holzmüller, Improved Approximation Schemes for the Restricted Shortest Path Problem, 2017 (https://arxiv.org/abs/1711.00284)

David Holzmüller, Efficient Neighbor-Finding on Space-Filling Curves, 2017 (https://arxiv.org/abs/1710.06384)

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