1 to 10 of 59 Results
Aug 8, 2024 -
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data
Plain Text - 129.4 GB -
MD5: 18626c6d8e091db924ce74542bb7df80
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Nov 5, 2024 -
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]
Plain Text - 141.2 GB -
MD5: a2cd5043064bd20b28f90dc724f7a4fd
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Aug 8, 2024
Holzmüller, David; Grinsztajn, Léo; Steinwart, Ingo, 2024, "Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", https://doi.org/10.18419/darus-4255, DaRUS, V1
This dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data". The main code is provided in pytabkit_code.zip and contains further documentation in README.md and the docs folder. The main code is also provided on Gi... |
Nov 5, 2024
Holzmüller, David; Grinsztajn, Léo; Steinwart, Ingo, 2024, "Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]", https://doi.org/10.18419/darus-4555, DaRUS, V1
This dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", specifically, the NeurIPS version which is also the second version on arXiv. The main code is provided in pytabkit_code.zip and contains further documen... |
Jun 20, 2022
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
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 GitHub. Information on the code and installation instructions can be found in the file README.md. |
Mar 23, 2021 -
Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression
Python Source Code - 16.2 KB -
MD5: 727ea9050972d03f12c6aa5b32b7e37c
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Python Source Code - 58 B -
MD5: 2b8520afbcb24f93040273f4edfee4b7
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Nov 5, 2024 -
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]
Plain Text - 90.5 GB -
MD5: ea203d35daf3fe13e8c32b93d0488c0c
Results for inner cross-validation / refitting of RealMLP-TD and LGBM-TD. |
Mar 23, 2021 -
Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression
Gzip Archive - 5.6 GB -
MD5: 220970917c2371206a808c02bbb9f359
Data generated by running the code |
Python Source Code - 19.2 KB -
MD5: 06303383461187b4df57f1e6a54afb12
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