PN 6

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

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11 to 20 of 48 Results

Extended Visual Analysis System for Scene-Graph-Based Visual Question Answering May 28, 2025 - PN 6-4 Schäfer, Noel; Künzel, Sebastian; Tilli, Pascal; Munz-Körner, Tanja; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2025, "Extended Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/DARUS-3909, DaRUS, V1 Source code of our extended visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Additionally, it is an improved version of our system that can be found here Instructions on how to use our system can be found in t...
Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models Apr 9, 2025 - PN 6A-4 Álvarez Chaves, Manuel, 2025, "Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models", https://doi.org/10.18419/DARUS-4920, DaRUS, V1, UNF:6:i3yM0uireOQetNHaTVPKfg== [fileUNF] Hybrid Models for Hydrology This repository contains code for the paper "When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models" submitted to Hydrology and Earth Systems Science (HESS). In this repository we have included the 'Hy2DL' and 'unite_toolbox' libraries in the versions they used f...
PN 6-8 (II)(Universität Stuttgart) Mar 28, 2025 Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...
PN 6-15(Universität Stuttgart) Nov 20, 2024 This Dataverse contains replication data and visualizations from Project Network 6-15: Machine Learning and Reservoir Computing with Many-Body Dynamics. The project is part of SimTech’s Project Network 6, Machine Learning for Simulation (https://www.simtech.uni-stuttgart.de/exc/research/pn/pn6/ ). The Dataverse includes simulations of non-equilibri...
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...
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data Aug 8, 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", 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...
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...
Hard Negative Captions(Universität Stuttgart) Jul 3, 2024PN 6-5 (II)
PN 6-5 (II)(Universität Stuttgart) Jul 3, 2024 SimTech Project PN 6-5 (II) "Interpretable and explainable cognitive inspired machine learning systems"
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...

Extended Visual Analysis System for Scene-Graph-Based Visual Question Answering

May 28, 2025 - PN 6-4

Schäfer, Noel; Künzel, Sebastian; Tilli, Pascal; Munz-Körner, Tanja; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2025, "Extended Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/DARUS-3909, DaRUS, V1

Source code of our extended visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Additionally, it is an improved version of our system that can be found here Instructions on how to use our system can be found in t...

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

Apr 9, 2025 - PN 6A-4

Álvarez Chaves, Manuel, 2025, "Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models", https://doi.org/10.18419/DARUS-4920, DaRUS, V1, UNF:6:i3yM0uireOQetNHaTVPKfg== [fileUNF]

Hybrid Models for Hydrology This repository contains code for the paper "When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models" submitted to Hydrology and Earth Systems Science (HESS). In this repository we have included the 'Hy2DL' and 'unite_toolbox' libraries in the versions they used f...

PN 6-8 (II)(Universität Stuttgart)

Mar 28, 2025

Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...

PN 6-15(Universität Stuttgart)

Nov 20, 2024

This Dataverse contains replication data and visualizations from Project Network 6-15: Machine Learning and Reservoir Computing with Many-Body Dynamics. The project is part of SimTech’s Project Network 6, Machine Learning for Simulation (https://www.simtech.uni-stuttgart.de/exc/research/pn/pn6/ ). The Dataverse includes simulations of non-equilibri...

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...

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

Aug 8, 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", 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...

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...

Hard Negative Captions(Universität Stuttgart)

Jul 3, 2024PN 6-5 (II)

PN 6-5 (II)(Universität Stuttgart)

Jul 3, 2024

SimTech Project PN 6-5 (II) "Interpretable and explainable cognitive inspired machine learning systems"

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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