D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David, Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner. 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.