David Holzmüller

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Ingo SteinwartUniversity of StuttgartVerified email at mathematik.uni-stuttgart.de
Johannes KästnerUniversity of StuttgartVerified email at theochem.uni-stuttgart.de
Viktor ZaverkinPhD Student, Institute for Theoretical Chemistry, University of StuttgartVerified email at theochem.uni-stuttgart.de
Daniel HaeufleUniversity of Tübingen, GermanyVerified email at uni-tuebingen.de
Danny DriessTU BerlinVerified email at ipvs.uni-stuttgart.de
Isabell WochnerUniversity of StuttgartVerified email at simtech.uni-stuttgart.de
Dirk PflügerUniversity of StuttgartVerified email at ipvs.uni-stuttgart.de

David Holzmüller

PhD Student, University of Stuttgart

Verified email at mathematik.uni-stuttgart.de - Homepage

Deep Learning Machine Learning Sparse Grids Space-Filling Curves Algorithms


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Efficient Neighbor-Finding on Space-Filling Curves D Holzmüller arXiv preprint arXiv:1710.06384, 2017	11	2017
Muscles reduce neuronal information load: quantification of control effort in biological vs. robotic pointing and walking DFB Haeufle, I Wochner, D Holzmüller, D Driess, M Günther, S Schmitt Frontiers in Robotics and AI 7, 77, 2020	8	2020
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent D Holzmüller, I Steinwart arXiv preprint arXiv:2002.04861, 2020	4	2020
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments V Zaverkin, D Holzmüller, I Steinwart, J Kästner Journal of Chemical Theory and Computation 17 (10), 6658-6670, 2021	2	2021
Improved approximation schemes for the restricted shortest path problem D Holzmüller arXiv preprint arXiv:1711.00284, 2017	2	2017
On the Universality of the Double Descent Peak in Ridgeless Regression D Holzmüller International Conference on Learning Representations 2021, 2020	1	2020
Convergence Analysis of Neural Networks D Holzmüller University of Stuttgart, 2019	1	2019
Predicting properties of periodic systems from cluster data: A case study of liquid water V Zaverkin, D Holzmüller, R Schuldt, J Kästner The Journal of Chemical Physics 156 (11), 114103, 2022		2022
A Framework and Benchmark for Deep Batch Active Learning for Regression D Holzmüller, V Zaverkin, J Kästner, I Steinwart arXiv preprint arXiv:2203.09410, 2022		2022
Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework D Holzmüller, D Pflüger Sparse Grids and Applications-Munich 2018, 69-100, 2021		2021

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