PN 6

SimTech EXC 2075 Project Network 6 "Machine learning for simulation"

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

631 to 640 of 867 Results

tex_tail.txt Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Plain Text - 124 B - MD5: 47aeb495fcff06691708ae2515da5e49
TrainingSetup.py Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Python Source Code - 11.2 KB - MD5: 73b9c67f5a7a2513bee4c2b93e33e34c
train_star_dataset.py Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Python Source Code - 20.0 KB - MD5: 92dd31d212970c4a430d131855d5631d
utils.py Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Python Source Code - 2.9 KB - MD5: 44757495ab97921515a4296648fed8a3
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Apr 13, 2022 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]", https://doi.org/10.18419/DARUS-2615, DaRUS, V1 This dataset contains code and data for our paper "A 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...
algorithms.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 26.1 KB - MD5: 135174e733df1a4b2987aa0cb450c4a3
analysis.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 22.1 KB - MD5: 50ac2de7ed2628a62f3ea8727751e027
benchmark.ipynb Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Jupyter Notebook - 111.5 KB - MD5: 96b0ac084968162443906c31b9a16dbf
check_task_learnability.py Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Python Source Code - 3.3 KB - MD5: 3589368ab168cf46c8a7222f1e2acf29
custom_paths.py.default Apr 13, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Unknown - 498 B - MD5: 0aa70e1f521326332f33f70b938dc292

tex_tail.txt

Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

Plain Text - 124 B -

TrainingSetup.py

Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

Python Source Code - 11.2 KB -

train_star_dataset.py

Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

Python Source Code - 20.0 KB -

utils.py

Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

Python Source Code - 2.9 KB -

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Apr 13, 2022

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]", https://doi.org/10.18419/DARUS-2615, DaRUS, V1

This dataset contains code and data for our paper "A 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...

algorithms.py

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

Python Source Code - 26.1 KB -

analysis.py

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

Python Source Code - 22.1 KB -

benchmark.ipynb

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

Jupyter Notebook - 111.5 KB -

check_task_learnability.py

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

Python Source Code - 3.3 KB -

custom_paths.py.default

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

Unknown - 498 B -

Add Data

Share Dataverse

Link Dataverse

Reset Modifications