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.

581 to 590 of 877 Results

ckpt-144.data-00000-of-00001 Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Unknown - 10.8 MB - MD5: 12c83e97acd358343f08d32cb5936292
ckpt-144.index Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Unknown - 2.0 KB - MD5: 5b0bc8ab52a26c6a409ae603e7044f45
ckpt-145.data-00000-of-00001 Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Unknown - 10.8 MB - MD5: 9b21aa7053bec0bdbb55a95484bf7ef5
ckpt-145.index Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Unknown - 2.0 KB - MD5: e3f90516fb4a2274760eddb390b46409
ckpt-158.data-00000-of-00001 Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Unknown - 10.8 MB - MD5: 48b5a5751180054789b3fa006e13d618
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Aug 24, 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 v2]", https://doi.org/10.18419/DARUS-3110, DaRUS, V1 This dataset contains code and data for the second arXiv version of 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. Informat...
algorithms.py Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Python Source Code - 26.9 KB - MD5: fbf003864bf024b0fd1fd8750f249ac2
analysis.py Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Python Source Code - 23.5 KB - MD5: d973bc30b1e74356761cb24935549c96
benchmark.ipynb Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Jupyter Notebook - 116.2 KB - MD5: 4694ad59499893469b596d47e84949cf
check_task_learnability.py Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Python Source Code - 3.3 KB - MD5: 3589368ab168cf46c8a7222f1e2acf29

ckpt-144.data-00000-of-00001

Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Unknown - 10.8 MB -

ckpt-144.index

Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Unknown - 2.0 KB -

ckpt-145.data-00000-of-00001

Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Unknown - 10.8 MB -

ckpt-145.index

Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Unknown - 2.0 KB -

ckpt-158.data-00000-of-00001

Feb 20, 2023 - Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Unknown - 10.8 MB -

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

Aug 24, 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 v2]", https://doi.org/10.18419/DARUS-3110, DaRUS, V1

This dataset contains code and data for the second arXiv version of 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. Informat...

algorithms.py

Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]

Python Source Code - 26.9 KB -

analysis.py

Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]

Python Source Code - 23.5 KB -

benchmark.ipynb

Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]

Jupyter Notebook - 116.2 KB -

check_task_learnability.py

Aug 24, 2022 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]

Python Source Code - 3.3 KB -

Add Data

Share Dataverse

Link Dataverse

Reset Modifications