PN 6-3

SimTech Project PN6-3 "Understanding Physical Constraints in Machine Learning for Simulation"

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

11 to 20 of 59 Results

pytabkit_code.zip Nov 5, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2] ZIP Archive - 719.6 KB - MD5: c4803f9ed6ed2be89668af29f9720404 Contains the code for running the benchmarks, as well as an implementation of the models evaluated on these benchmarks. Also contains instructions in README.md as well as the documentation.
RealMLP-HPO_steps.tar.gz 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 - 140.7 GB - MD5: 85e866f98e86d7d2dfdb39fcc1cea44c DATA
ResNet-RTDL-HPO_steps.tar.gz 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 - 153.3 GB - MD5: 59c1d707c1b98aa50ae75390e20dc86b DATA
results_main.tar.gz 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 - 182.6 GB - MD5: c51b4d12201d3d24cf6ff8868856ef65 DATA Detailed results, including predictions on datasets and best parameters, for all main methods. This excludes the data for individual hyperparameter optimization (HPO) steps, which is provided separately.
results_small.tar.gz 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 - 5.7 GB - MD5: 45aa6445bbe5308a70cc4f5541b85acc DATA Results folder (within the data folder) without extra results (predictions on the datasets, optimal hyperparameters). After unpacking, the folder should be renamed to "results".
RF-HPO_steps.tar.gz 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 - 9.1 GB - MD5: 4b57b6c90b0f08821b08dd09687b8d10 DATA
TabR-HPO_steps.tar.gz 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 - 8.9 GB - MD5: 5e75ae55be54732cb0825f15bfe964fe DATA
tasks.tar.gz 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 - 3.5 GB - MD5: aee96049f206a64cf9002749f238a8b9 DATA Folder with imported datasets, with restricted access for copyright reasons.
XGB-HPO_steps.tar.gz 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 - 120.7 GB - MD5: 7c82fcd9a59eac9f2736d02970010233 DATA
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data 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 GitHub. Here, we additionally provide the data that is generated by the...

pytabkit_code.zip

Nov 5, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]

ZIP Archive - 719.6 KB -

Contains the code for running the benchmarks, as well as an implementation of the models evaluated on these benchmarks. Also contains instructions in README.md as well as the documentation.

RealMLP-HPO_steps.tar.gz

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 - 140.7 GB -

DATA

ResNet-RTDL-HPO_steps.tar.gz

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 - 153.3 GB -

DATA

results_main.tar.gz

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 - 182.6 GB -

DATA

Detailed results, including predictions on datasets and best parameters, for all main methods. This excludes the data for individual hyperparameter optimization (HPO) steps, which is provided separately.

results_small.tar.gz

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 - 5.7 GB -

DATA

Results folder (within the data folder) without extra results (predictions on the datasets, optimal hyperparameters). After unpacking, the folder should be renamed to "results".

RF-HPO_steps.tar.gz

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 - 9.1 GB -

DATA

TabR-HPO_steps.tar.gz

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 - 8.9 GB -

DATA

tasks.tar.gz

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 - 3.5 GB -

DATA

Folder with imported datasets, with restricted access for copyright reasons.

XGB-HPO_steps.tar.gz

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 - 120.7 GB -

DATA

Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

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 GitHub. Here, we additionally provide the data that is generated by the...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications