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Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression arXiv v2

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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.
DOI: 10.18419/darus-3110

Abstract

This dataset contains code and data for the second arXiv version of our paper Ä Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The code is also available on GitHub. Information on the code can be found in the file README.md and in the Jupyter notebooks in the examples folder. Additionally, we provide the files results.tar.gz and plots.tar.gz which contain generated data and plots. These files can be unpacked in folders specified in custom_paths.py (see README.md) and can be used as described in examples/benchmark.ipynb.

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