David Holzmüller
David Holzmüller
Postdoc, INRIA
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
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
A Framework and Benchmark for Deep Batch Active Learning for Regression
D Holzmüller, V Zaverkin, J Kästner, I Steinwart
Journal of Machine Learning Research 24 (164), 1-81, 2023
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
Exploring chemical and conformational spaces by batch mode deep active learning
V Zaverkin, D Holzmüller, I Steinwart, J Kästner
Digital Discovery 1 (5), 605-620, 2022
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, 77, 2020
Transfer learning for chemically accurate interatomic neural network potentials
V Zaverkin, D Holzmüller, L Bonfirraro, J Kästner
Physical Chemistry Chemical Physics 25 (7), 5383-5396, 2023
On the Universality of the Double Descent Peak in Ridgeless Regression
D Holzmüller
International Conference on Learning Representations 2021, 2020
Efficient Neighbor-Finding on Space-Filling Curves
D Holzmüller
arXiv preprint arXiv:1710.06384, 2017
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
D Holzmüller, F Bach
arXiv preprint arXiv:2303.03237, 2023
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
D Holzmüller, I Steinwart
Journal of Machine Learning Research 23 (181), 1-82, 2022
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
M Haas*, D Holzmüller*, U von Luxburg, I Steinwart
NeurIPS 2023, 2023
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
V Zaverkin, D Holzmüller, H Christiansen, F Errica, F Alesiani, ...
arXiv preprint arXiv:2312.01416, 2023
Improved approximation schemes for the restricted shortest path problem
D Holzmüller
arXiv preprint arXiv:1711.00284, 2017
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
Convergence Analysis of Neural Networks
D Holzmüller
University of Stuttgart, 2019
Regression from linear models to neural networks: double descent, active learning, and sampling
D Holzmüller
University of Stuttgart, 2023
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