PUMA

academical publication management
collect, organize, and share publications

( en | de )

 

Disambiguation of "Zaverkin, Viktor"

Author of the publication

copydeleteadd this publication to your clipboard
A Framework and Benchmark for Deep Batch Active Learning for Regression

, , , and . Journal of Machine Learning Research, 24 (164): 1--81 (2023)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Viktor Zaverkin University of Stuttgart

V. Zaverkin

apl. Prof. Dr. Viktor Avrutin University of Stuttgart

 

Other publications of authors with the same name

A Framework and Benchmark for Deep Batch Active Learning for Regression, , , and . Journal of Machine Learning Research, 24 (164): 1--81 (2023)Transfer learning for chemically accurate interatomic neural network potentials, , , and . Phys. Chem. Chem. Phys., 25 (7): 5383-5396 (2023)Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion, , and . Mon. Not. R. Astron. Soc., 510 (2): 3063-3070 (2022)Performance of two complementary machine-learned potentials in modelling chemically complex systems, , , , , and . Npj Comput. Mater., (2023)Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design, and . Machine Learning : Science and Technology, 2 (3): 035009 (2021)Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments, , , and . Software, (2021)Related to: 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). doi: 10.1021/acs.jctc.1c00527.A Framework and Benchmark for Deep Batch Active Learning for Regression, , , and . J. Mach. Learn. Res., 24 (164): 1–81 (2023)Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments, , , , and . J. Chem. Theory Comput., (2022)Transfer learning for chemically accurate interatomic neural network potentials, , , and . Phys. Chem. Chem. Phys., 25 (7): 5383-5396 (2023)Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion, , and . Mon. Not. R. Astron. Soc., 510 (2): 3063-3070 (2021)