10.18419/darus-2615Holzmüller, DavidDavidHolzmüller0000-0002-9443-0049Universität StuttgartZaverkin, ViktorViktorZaverkin0000-0001-9940-8548Universität StuttgartKästner, JohannesJohannesKästner0000-0001-6178-7669Universität StuttgartSteinwart, IngoIngoSteinwart0000-0002-4436-7109Universität StuttgartCode and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]DaRUS2022Computer and Information ScienceMathematical SciencesActive LearningDeep LearningArtificial Neural NetworkRegressionHolzmüller, DavidDavidHolzmüllerUniversität StuttgartHolzmüller, DavidDavidHolzmüllerUniversität StuttgartSteinwart, IngoIngoSteinwartUniversität Stuttgart2022-03-142022-04-132203.0941026715226551142033368498157261060433810269052312041911191682723700875340111135764539452095868283185212217460694461229650326535236991516715551105718018375403010text/x-pythontext/x-pythonapplication/x-ipynb+jsontext/x-pythonapplication/octet-streamtext/x-pythonapplication/gziptext/x-pythontext/x-pythontext/x-pythontext/x-pythonapplication/x-ipynb+jsontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/plain; charset=US-ASCIItext/x-pythontext/plain; charset=US-ASCIIapplication/gziptext/x-pythontext/markdowntext/x-pythontext/plainapplication/gziptext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythonapplication/x-ipynb+jsontext/x-pythontext/x-python1.0This dataset contains code and data for our paper <a href=https://arxiv.org/abs/2203.09410v1>"A Framework and Benchmark for Deep Batch Active Learning for Regression"</a>. 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 <a href=https://github.com/dholzmueller/bmdal_reg>GitHub</a>. Information on the code can be found in the file <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=102389">README.md</a> and in the Jupyter notebooks in the examples folder. Additionally, we provide the files <code>results.tar.gz</code> and <code>plots.tar.gz</code> which contain generated data and plots. These files can be unpacked in folders specified in <code>custom_paths.py</code> (see <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=102389">README.md</a>) and can be used as described in <code>examples/benchmark.ipynb</code>.Basic instructions for installing and running the software can be found in the <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=102389">README.md</a> file.DFGEXC 2075 - 390740016German Academic Scholarship Foundation