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run_evaluation.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 6.4 KB - MD5: 4539bc283a5ce2b82b740bc067f46b78
run_experiments.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 25.9 KB - MD5: 7f28748e9afb84ca52a5675e4f8d015f
selection.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 23.1 KB - MD5: f12fee1ca7841cea0aa388a78b891918
task_execution.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 14.8 KB - MD5: fd25c6d28a3f91c7023cee034c38129a
test_single_task.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 1.5 KB - MD5: 76d34c39e3698ca5bcaca1366639632a
train.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 10.8 KB - MD5: 7aba787aeb378b4cc8e910d38491b362
using_bmdal.ipynb Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Jupyter Notebook - 176.0 KB - MD5: f0a3c1375b6a5c208c82defde288feca
utils.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 7.4 KB - MD5: f612e49528fcb4c2b9edd09209db527a
visualize_lcmd.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 2.9 KB - MD5: 73fa0d1da429485890f0c8f3845303e4
Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework Mar 29, 2022 Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/DARUS-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF] This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which was used to generate the runtime plot in the paper. For more details,...

run_evaluation.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 6.4 KB -

run_experiments.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 25.9 KB -

selection.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 23.1 KB -

task_execution.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 14.8 KB -

test_single_task.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 1.5 KB -

train.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 10.8 KB -

using_bmdal.ipynb

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Jupyter Notebook - 176.0 KB -

utils.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 7.4 KB -

visualize_lcmd.py

Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Python Source Code - 2.9 KB -

Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework

Mar 29, 2022

Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/DARUS-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF]

This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which was used to generate the runtime plot in the paper. For more details,...