|
View: |
Part 1: Document Description
|
|
Citation |
|
|---|---|
|
Title: |
Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent |
|
Identification Number: |
doi:10.18419/darus-2978 |
|
Distributor: |
DaRUS |
|
Date of Distribution: |
2022-06-20 |
|
Version: |
1 |
|
Bibliographic Citation: |
Holzmüller, David; Steinwart, Ingo, 2022, "Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent", https://doi.org/10.18419/darus-2978, DaRUS, V1 |
|
Citation |
|
|
Title: |
Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent |
|
Identification Number: |
doi:10.18419/darus-2978 |
|
Authoring Entity: |
Holzmüller, David (Universität Stuttgart) |
|
Steinwart, Ingo (Universität Stuttgart) |
|
|
Grant Number: |
EXC 2075 - 390740016 |
|
Distributor: |
DaRUS |
|
Access Authority: |
Holzmüller, David |
|
Access Authority: |
Steinwart, Ingo |
|
Depositor: |
Holzmüller, David |
|
Date of Deposit: |
2022-06-02 |
|
Holdings Information: |
https://doi.org/10.18419/darus-2978 |
|
Study Scope |
|
|
Keywords: |
Computer and Information Science, Mathematical Sciences, Artificial Neural Network, Regression |
|
Abstract: |
This data set contains code used to generate figures and tables in our paper "Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent". The code is also available on <a href=https://github.com/dholzmueller/nn_inconsistency>GitHub</a>. Information on the code and installation instructions can be found in the file README.md. |
|
Notes: |
Basic instructions for installing and running the software can be found in the README.md file. |
|
Methodology and Processing |
|
|
Sources Statement |
|
|
Data Access |
|
|
Other Study Description Materials |
|
|
Related Publications |
|
|
Citation |
|
|
Title: |
David Holzmüller and Ingo Steinwart. Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, 2020. |
|
Identification Number: |
2002.04861 |
|
Bibliographic Citation: |
David Holzmüller and Ingo Steinwart. Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, 2020. |
|
Label: |
custom_paths.py |
|
Notes: |
text/x-python |
|
Label: |
eval_nn_setups.py |
|
Notes: |
text/x-python |
|
Label: |
eval_star_dataset.py |
|
Notes: |
text/x-python |
|
Label: |
LICENSE |
|
Notes: |
text/plain; charset=US-ASCII |
|
Label: |
mc_event_estimation.py |
|
Notes: |
text/x-python |
|
Label: |
mc_plotting.py |
|
Notes: |
text/x-python |
|
Label: |
mc_sgd_keras.py |
|
Notes: |
text/x-python |
|
Label: |
mc_training.py |
|
Notes: |
text/x-python |
|
Label: |
plot_examples.py |
|
Notes: |
text/x-python |
|
Label: |
README.md |
|
Notes: |
text/markdown |
|
Label: |
requirements.txt |
|
Notes: |
text/plain |
|
Label: |
run_nn_setups.py |
|
Notes: |
text/x-python |
|
Label: |
show_training.py |
|
Notes: |
text/x-python |
|
Label: |
tex_head.txt |
|
Notes: |
text/plain |
|
Label: |
tex_tail.txt |
|
Notes: |
text/plain |
|
Label: |
TrainingSetup.py |
|
Notes: |
text/x-python |
|
Label: |
train_star_dataset.py |
|
Notes: |
text/x-python |
|
Label: |
utils.py |
|
Notes: |
text/x-python |