Publications

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]. 2023. [PUMA: darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 ubs_40202 unibibliografie]

Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, and Johannes Kästner. Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials. 2023. [PUMA: darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 unibibliografie]

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]. 2022. [PUMA: darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 ubs_40202 unibibliografie]

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]. 2022. [PUMA: darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 ubs_40202 unibibliografie]

Viktor Zaverkin, David Holzmüller, Ingo Steinwart, and Johannes Kästner. Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. 2021. [PUMA: darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 ubs_40202 unibibliografie]