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README.md Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Markdown Text - 5.3 KB - MD5: 681760ae446fff1be139c4ff640fb034
requirements.txt Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Plain Text - 637 B - MD5: 09b283d76024ec31e9134f8d4bf7636d
run_nn_setups.py Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Python Source Code - 30.7 KB - MD5: ea8311dc8c3e079b3caa9a1c6774412a
show_training.py Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Python Source Code - 17.1 KB - MD5: c0aeba6d65554d5395ca28296a781ea0
tex_head.txt Jun 20, 2022 - Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Plain Text - 4.9 KB - MD5: 4c5247ed7dab1c2c29f8e470d1dc271e
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...

README.md

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

Markdown Text - 5.3 KB -

requirements.txt

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

Plain Text - 637 B -

run_nn_setups.py

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

Python Source Code - 30.7 KB -

show_training.py

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

Python Source Code - 17.1 KB -

tex_head.txt

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

Plain Text - 4.9 KB -

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...