PN 6

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

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471 to 480 of 877 Results

test-log_sum_exp.R May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison R Syntax - 117 B - MD5: 2b0519a5f7be7f5bf1d6fb09d4f6aeb7
testthat.R May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison R Syntax - 84 B - MD5: c9e4897898f5627111b8ce12e5f4daaf
Data and Code for: Meta-Uncertainty in Bayesian Model Comparison(Universität Stuttgart) May 17, 2023 Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies.
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Apr 5, 2023 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/DARUS-3394, DaRUS, V1 This dataset contains code and data for the third 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. Informati...
algorithms.py Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Python Source Code - 26.9 KB - MD5: 8992d130a29ce591cf770f831365e711
analysis.py Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Python Source Code - 24.0 KB - MD5: e7e55610a01b7baf79167d03185187bb
benchmark.ipynb Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Jupyter Notebook - 116.3 KB - MD5: b64ba74944c8c63b6cdbcb5c3f3c3ca5
check_task_learnability.py Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Python Source Code - 3.4 KB - MD5: 145530092e17ac5242b1395550fde713
custom_paths.py Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Python Source Code - 765 B - MD5: a795511ba49a289abf2544960864bec4
custom_paths.py.default Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Unknown - 680 B - MD5: 59cf295649a62521f8cb575c1538a133

test-log_sum_exp.R

May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison

R Syntax - 117 B -

testthat.R

May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison

R Syntax - 84 B -

Data and Code for: Meta-Uncertainty in Bayesian Model Comparison(Universität Stuttgart)

May 17, 2023

Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies.

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Apr 5, 2023

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/DARUS-3394, DaRUS, V1

This dataset contains code and data for the third 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. Informati...

algorithms.py

Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Python Source Code - 26.9 KB -

analysis.py

Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Python Source Code - 24.0 KB -

benchmark.ipynb

Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Jupyter Notebook - 116.3 KB -

check_task_learnability.py

Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Python Source Code - 3.4 KB -

custom_paths.py

Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Python Source Code - 765 B -

custom_paths.py.default

Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Unknown - 680 B -

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