E-LIB Suche - Ergebnisse für: Holzmüller, David

1

Uncertainty-biased molecular dynamics for learning uniforml..:

Zaverkin, Viktor ; Holzmüller, David ; Christiansen, Henrik...
npj Computational Materials. 10 (2024) 1 - p. , 2024

2

Transfer learning for chemically accurate interatomic neura..:

Zaverkin, Viktor ; Holzmüller, David ; Bonfirraro, Luca.
Physical Chemistry Chemical Physics. 25 (2023) 7 - p. 5383-5396 , 2023

Link: https://doi.org/10.1039/..

3

Exploring chemical and conformational spaces by batch mode ..:

Zaverkin, Viktor ; Holzmüller, David ; Steinwart, Ingo.
Digital Discovery. 1 (2022) 5 - p. 605-620 , 2022

Link: https://doi.org/10.1039/..

4

Predicting properties of periodic systems from cluster data..:

Zaverkin, Viktor ; Holzmüller, David ; Schuldt, Robin.
The Journal of Chemical Physics. 156 (2022) 11 - p. , 2022

Link: https://doi.org/10.1063/..

5

Fast Sparse Grid Operations Using the Unidirectional Princi..:

, In: Lecture Notes in Computational Science and Engineering; Sparse Grids and Applications - Munich 2018,

Holzmüller, David ; Pflüger, Dirk - p. 69-100 , 2021

Link: https://doi.org/10.1007/..

6

Fast and Sample-Efficient Interatomic Neural Network Potent..:

Zaverkin, Viktor ; Holzmüller, David ; Steinwart, Ingo.
Journal of Chemical Theory and Computation. 17 (2021) 10 - p. 6658-6670 , 2021

Link: https://doi.org/10.1021/..

7

Muscles Reduce Neuronal Information Load: Quantification of..:

Haeufle, Daniel F. B. ; Wochner, Isabell ; Holzmüller, David...
Frontiers in Robotics and AI. 7 (2020) - p. , 2020

Link: https://doi.org/10.3389/..

8

Convergence Rates for Non-Log-Concave Sampling and Log-Part..:

Holzmüller, David ; Bach, Francis
info:eu-repo/grantAgreement//724063/EU/Robust algorithms for learning from modern data/SEQUOIA. , 2023

Link: https://hal.science/hal-..

9

Convergence Rates for Non-Log-Concave Sampling and Log-Part..:

Holzmüller, David ; Bach, Francis
info:eu-repo/grantAgreement//724063/EU/Robust algorithms for learning from modern data/SEQUOIA. , 2023

Link: https://hal.science/hal-..

10

Convergence Rates for Non-Log-Concave Sampling and Log-Part..:

Holzmüller, David ; Bach, Francis
http://arxiv.org/abs/2303.03237. , 2023

Link: http://arxiv.org/abs/230..

11

Regression from linear models to neural networks : double d..:

Holzmüller, David
1859132677. , 2023

Link: https://doi.org/10.18419..

12

Uncertainty-biased molecular dynamics for learning uniforml..:

Zaverkin, Viktor ; Holzmüller, David ; Christiansen, Henrik...
http://arxiv.org/abs/2312.01416. , 2023

Link: http://arxiv.org/abs/231..

13

Predicting Properties of Periodic Systems from Cluster Data..:

Zaverkin, Viktor ; Holzmüller, David ; Schuldt, Robin.
http://arxiv.org/abs/2312.01414. , 2023

Link: http://arxiv.org/abs/231..

14

Mind the spikes: Benign overfitting of kernels and neural n..:

Haas, Moritz ; Holzmüller, David ; von Luxburg, Ulrike.
http://arxiv.org/abs/2305.14077. , 2023

Link: http://arxiv.org/abs/230..

15

Transfer learning for chemically accurate interatomic neura..:

Zaverkin, Viktor ; Holzmüller, David ; Bonfirraro, Luca.
http://arxiv.org/abs/2212.03916. , 2022

Link: http://arxiv.org/abs/221..