PN 6

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

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4,311 to 4,320 of 4,903 Results

pytabkit_code.zip Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data ZIP Archive - 6.9 MB - MD5: 80e03d81f1beb8a2173d5c38da4f493c Contains the code for running the meta-train and meta-test 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 Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data Plain Text - 128.6 GB - MD5: cecba6379df5f6ba81fcc51e7f087619 DATA
results_small.tar.gz Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data Plain Text - 4.2 GB - MD5: 19babc91c7dab580a598551191da968b DATA Results folder (within the data folder) without extra results (predictions on the datasets, optimal hyperparameters).
tasks.tar.gz Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data Plain Text - 3.1 GB - MD5: b224a117735d2e56bfd8c1546530a4c2 DATA Downloaded datasets, with restricted access for copyright reasons.
XGB-HPO_steps.tar.gz Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data Plain Text - 110.4 GB - MD5: e4671ada3b462010955e4da02dd9687c DATA
Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities Jul 8, 2024 - Hard Negative Captions Tilli, Pascal, 2024, "Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities", https://doi.org/10.18419/DARUS-4341, DaRUS, V1 Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image–text pairs, models fail to show fine-grained understanding of the combined semantics of these modalities. To this end, we propose Hard N...
hnc_clean_strict_train.json Jul 8, 2024 - Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities JSON - 7.6 GB - MD5: d2ed063924be0ead2b1475deeb001cf6 HNC training set, automatically generated.
hnc_clean_strict_val.json Jul 8, 2024 - Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities JSON - 1.1 GB - MD5: 6fe56c1128fa3615bcd41dd281636578 HNC validation set, automatically generated.
test_set_v1-0.json Jul 8, 2024 - Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities JSON - 411.7 KB - MD5: 22dcb8f1f3ab6497b5c4db5f3d756726 HNC test set, annotated by humans.
UNITE Toolbox Jun 18, 2024 - PN 6A-4 Álvarez Chaves, Manuel; Ehret, Uwe; Guthke, Anneli, 2024, "UNITE Toolbox", https://doi.org/10.18419/DARUS-4188, DaRUS, V1 UNITE Toolbox Unified diagnostic evaluation of scientific models based on information theory The UNITE Toolbox is a Python library for incorporating Information Theory into data analysis and modeling workflows. The toolbox collects different methods of estimating information-theoretic quantities in one easy-to-use Python package. Currently, UNITE i...

pytabkit_code.zip

Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

ZIP Archive - 6.9 MB -

Contains the code for running the meta-train and meta-test 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

Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

Plain Text - 128.6 GB -

DATA

results_small.tar.gz

Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

Plain Text - 4.2 GB -

DATA

Results folder (within the data folder) without extra results (predictions on the datasets, optimal hyperparameters).

tasks.tar.gz

Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

Plain Text - 3.1 GB -

DATA

Downloaded datasets, with restricted access for copyright reasons.

XGB-HPO_steps.tar.gz

Aug 8, 2024 - Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

Plain Text - 110.4 GB -

DATA

Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities

Jul 8, 2024 - Hard Negative Captions

Tilli, Pascal, 2024, "Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities", https://doi.org/10.18419/DARUS-4341, DaRUS, V1

Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image–text pairs, models fail to show fine-grained understanding of the combined semantics of these modalities. To this end, we propose Hard N...

hnc_clean_strict_train.json

Jul 8, 2024 - Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities

JSON - 7.6 GB -

HNC training set, automatically generated.

hnc_clean_strict_val.json

Jul 8, 2024 - Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities

JSON - 1.1 GB -

HNC validation set, automatically generated.

test_set_v1-0.json

Jul 8, 2024 - Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities

JSON - 411.7 KB -

HNC test set, annotated by humans.

UNITE Toolbox

Jun 18, 2024 - PN 6A-4

Álvarez Chaves, Manuel; Ehret, Uwe; Guthke, Anneli, 2024, "UNITE Toolbox", https://doi.org/10.18419/DARUS-4188, DaRUS, V1

UNITE Toolbox Unified diagnostic evaluation of scientific models based on information theory The UNITE Toolbox is a Python library for incorporating Information Theory into data analysis and modeling workflows. The toolbox collects different methods of estimating information-theoretic quantities in one easy-to-use Python package. Currently, UNITE i...

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