PN 6

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

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241 to 250 of 867 Results

toy-models-plots.ipynb Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Jupyter Notebook - 77.9 KB - MD5: 64985de605105262d97f2e9e4a4a8556
toy_models.py Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Python Source Code - 18.4 KB - MD5: 4fc8b8900e22952ac96c1f2a77635b92
training_hybrid.py Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Python Source Code - 12.3 KB - MD5: 3f8043e07e594648e098a3b5045aee5d
training_lstm.py Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Python Source Code - 10.3 KB - MD5: ae4b05e3488f75cf7a425a65ab988d7b
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.

toy-models-plots.ipynb

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

Jupyter Notebook - 77.9 KB -

toy_models.py

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

Python Source Code - 18.4 KB -

training_hybrid.py

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

Python Source Code - 12.3 KB -

training_lstm.py

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

Python Source Code - 10.3 KB -

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.

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