1 to 10 of 24 Results
Plain Text - 144 B -
MD5: efe4a073ed74cb1b591fed82e0ea00be
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Markdown Text - 2.4 KB -
MD5: f0f8fd6035ca53c0fc1541d1eed5ee8a
This file describes how to use the model. Please read it to find out how to reproduce the results from the paper. |
Jul 11, 2023 -
preCICE Distribution Version v2211.0
Markdown Text - 802 B -
MD5: b60bd9a3bef4af5f795ed46a693a9516
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Markdown Text - 4.8 KB -
MD5: f5c46b519720bc0a931c1370a2340017
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Markdown Text - 1.7 KB -
MD5: 8b77a53081e7377470d98324caba9d09
Description of this data set |
Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
Markdown Text - 5.6 KB -
MD5: 39f670a2010544495bb5a3a68ec36478
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Apr 5, 2023 -
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Plain Text - 11.1 KB -
MD5: e3fc50a88d0a364313df4b21ef20c29e
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Apr 5, 2023 -
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Plain Text - 394 B -
MD5: e1c00007200f1f72eba5102e4795d31b
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Apr 5, 2023 -
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Markdown Text - 10.7 KB -
MD5: 82e3ccaeb32fd5e3c80d16ff9d23b8fb
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Apr 5, 2023 -
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Plain Text - 606 B -
MD5: 84f37d69730eca561d54ed4199e97a62
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