PN 6

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

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4,281 to 4,290 of 4,903 Results

training.py Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Python Source Code - 975 B - MD5: bdfcdc29a422859173711c627dc6878e
training_toy_models.py Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Python Source Code - 21.3 KB - MD5: 933984ef65287c1314b907e614c6067c
unite_toolbox.zip Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models ZIP Archive - 18.4 KB - MD5: 934d31b87c9e5e48157bfa0734edaea9
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2] Nov 5, 2024 - PN 6-3 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 documentation in README.md and the docs folder. The main code is also provide...
CatBoost-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 - 141.2 GB - MD5: a2cd5043064bd20b28f90dc724f7a4fd DATA
cv_refit.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 - 90.5 GB - MD5: ea203d35daf3fe13e8c32b93d0488c0c DATA Results for inner cross-validation / refitting of RealMLP-TD and LGBM-TD.
FTT-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 - 16.7 GB - MD5: 6caba3a60faaad1b7a91b03e06c035d2 DATA
grinsztajn_benchmarking_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 - 1.1 MB - MD5: e4e6f5dca291e6c59f5135ab1a1d63a8 Code for running the old version of the Grinsztajn. et al (2022) benchmark with our models.
grinsztajn_results.csv.gz Nov 5, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2] Gzip Archive - 87.2 MB - MD5: 04efd949409a882d3fb3c18371d451fa Results on the old version of the Grinsztajn et al. (2022) benchmark.
LGBM-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 - 118.8 GB - MD5: d02951865e99ef832a137804cce02b47 DATA

training.py

Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models

Python Source Code - 975 B -

training_toy_models.py

Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models

Python Source Code - 21.3 KB -

unite_toolbox.zip

Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models

ZIP Archive - 18.4 KB -

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

Nov 5, 2024 - PN 6-3

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 documentation in README.md and the docs folder. The main code is also provide...

CatBoost-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 - 141.2 GB -

DATA

cv_refit.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 - 90.5 GB -

DATA

Results for inner cross-validation / refitting of RealMLP-TD and LGBM-TD.

FTT-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 - 16.7 GB -