PN 6

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

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1 to 10 of 26 Results

Visual Analytics System for Hidden States in Recurrent Neural Networks Sep 10, 2021 - PN 6-4 Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/darus-2052, DaRUS, V1 Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analy...
Visual Analysis System for Scene-Graph-Based Visual Question Answering Jul 25, 2023 - PN 6-4 Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/darus-3589, DaRUS, V1 Source code of our 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. Instructions on how to use our system can be found in the README.
Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games Nov 2, 2020 - PN 6-4 Munz, Tanja; Schäfer, Noel; Blascheck, Tanja; Kurzhals, Kuno; Zhang, Eugene; Weiskopf, Daniel, 2020, "Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games", https://doi.org/10.18419/darus-1130, DaRUS, V1 Supplemental material for "Comparative Visual Gaze Analysis for Virtual Board Games". Contains the source code, an executable file, an example data set, a video for demonstration of the analysis system and additional information about Go. The analysis system supports eye tracking...
Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models" Jul 25, 2023 - PN 6-4 Munz-Körner, Tanja; Künzel, Sebastian; Weiskopf, Daniel, 2023, "Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"", https://doi.org/10.18419/darus-3456, DaRUS, V1 Supplemental material for the poster "Visual-Explainable AI: The Use Case of Language Models" published at the International Conference on Data-Integrated Simulation Science 2023. Collection of videos and images showing our interactive visualization systems for exploring language...
Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression Mar 23, 2021 - PN 6-3 Holzmüller, David, 2021, "Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression", https://doi.org/10.18419/darus-1771, DaRUS, V1 This dataset contains code used to generate the figures in the paper On the Universality of the Double Descent Peak in Ridgeless Regression, David Holzmüller, International Conference on Learning Representations 2021. The code is also provided on GitHub. Here, we additionally pro...
Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework Mar 29, 2022 Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/darus-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF] This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which...
Replication Data for: Evaluating Non-Parametric Methods in Information Theory Mar 14, 2024 - PN 6A-4 Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: Evaluating Non-Parametric Methods in Information Theory", https://doi.org/10.18419/darus-4087, DaRUS, V1 Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "Evaluating Density- and Nearest Neighbor-based Methods to Accurately Estimate Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as...
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
Replication Code for: Meta-Uncertainty in Bayesian Model Comparison May 25, 2023 - Data and Code for: Meta-Uncertainty in Bayesian Model Comparison Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/darus-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF] This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper)....
PyPlant: A Python Framework for Cached Function Pipelines Jan 24, 2022 - PN 6-6 Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/darus-2249, DaRUS, V1 PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed.

Visual Analytics System for Hidden States in Recurrent Neural Networks

Sep 10, 2021 - PN 6-4

Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/darus-2052, DaRUS, V1

Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analy...

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

Jul 25, 2023 - PN 6-4

Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/darus-3589, DaRUS, V1

Source code of our 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. Instructions on how to use our system can be found in the README.

Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games

Nov 2, 2020 - PN 6-4

Munz, Tanja; Schäfer, Noel; Blascheck, Tanja; Kurzhals, Kuno; Zhang, Eugene; Weiskopf, Daniel, 2020, "Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games", https://doi.org/10.18419/darus-1130, DaRUS, V1

Supplemental material for "Comparative Visual Gaze Analysis for Virtual Board Games". Contains the source code, an executable file, an example data set, a video for demonstration of the analysis system and additional information about Go. The analysis system supports eye tracking...

Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"

Jul 25, 2023 - PN 6-4

Munz-Körner, Tanja; Künzel, Sebastian; Weiskopf, Daniel, 2023, "Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"", https://doi.org/10.18419/darus-3456, DaRUS, V1

Supplemental material for the poster "Visual-Explainable AI: The Use Case of Language Models" published at the International Conference on Data-Integrated Simulation Science 2023. Collection of videos and images showing our interactive visualization systems for exploring language...

Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression

Mar 23, 2021 - PN 6-3

Holzmüller, David, 2021, "Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression", https://doi.org/10.18419/darus-1771, DaRUS, V1

This dataset contains code used to generate the figures in the paper On the Universality of the Double Descent Peak in Ridgeless Regression, David Holzmüller, International Conference on Learning Representations 2021. The code is also provided on GitHub. Here, we additionally pro...

Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework

Mar 29, 2022

Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/darus-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF]

This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which...

Replication Data for: Evaluating Non-Parametric Methods in Information Theory

Mar 14, 2024 - PN 6A-4

Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: Evaluating Non-Parametric Methods in Information Theory", https://doi.org/10.18419/darus-4087, DaRUS, V1

Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "Evaluating Density- and Nearest Neighbor-based Methods to Accurately Estimate Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as...

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

Replication Code for: Meta-Uncertainty in Bayesian Model Comparison

May 25, 2023 - Data and Code for: Meta-Uncertainty in Bayesian Model Comparison

Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/darus-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF]

This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper)....

PyPlant: A Python Framework for Cached Function Pipelines

Jan 24, 2022 - PN 6-6

Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/darus-2249, DaRUS, V1

PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed.

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