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20,281 to 20,290 of 20,843 Results

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
Extended Hill-Type Muscle Material Model (EHTM)(Universität Stuttgart) Apr 6, 2022 This Dataverse contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element solver LS-DYNA.

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 -

Extended Hill-Type Muscle Material Model (EHTM)(Universität Stuttgart)

Apr 6, 2022

This Dataverse contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element solver LS-DYNA.

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