PN 6

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

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1 to 10 of 26 Results

PN 6-4(Universität Stuttgart) Oct 27, 2020 SimTech Project PN 6-4 "Visual Analytics for Deep Learning"
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...
PN 6-3(Institute for Stochastics and Applications, Universität Stuttgart) Mar 16, 2021 SimTech Project PN6-3 "Understanding Physical Constraints in Machine Learning for Simulation"
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...
NMTVis - Neural Machine Translation Visualization System May 26, 2021 - PN 6-4 Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Neural Machine Translation Visualization System", https://doi.org/10.18419/darus-1849, DaRUS, V1 NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afte...
NMTVis - Trained Models for our Visual Analytics System May 26, 2021 - PN 6-4 Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Trained Models for our Visual Analytics System", https://doi.org/10.18419/darus-1850, DaRUS, V1 Trained models and vocabulary files for the use in our visual analytics system NMTVis. There are models for German to English and vice versa available for an LSTM-based and the Transformer architecture.
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...
PN 6-6(Universität Stuttgart) Sep 29, 2021 SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"
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.
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Oct 15, 2021 Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1 Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

PN 6-4(Universität Stuttgart)

Oct 27, 2020

SimTech Project PN 6-4 "Visual Analytics for Deep Learning"

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...

PN 6-3(Institute for Stochastics and Applications, Universität Stuttgart)

Mar 16, 2021

SimTech Project PN6-3 "Understanding Physical Constraints in Machine Learning for Simulation"

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...

NMTVis - Neural Machine Translation Visualization System

May 26, 2021 - PN 6-4

Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Neural Machine Translation Visualization System", https://doi.org/10.18419/darus-1849, DaRUS, V1

NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afte...

NMTVis - Trained Models for our Visual Analytics System

May 26, 2021 - PN 6-4

Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Trained Models for our Visual Analytics System", https://doi.org/10.18419/darus-1850, DaRUS, V1

Trained models and vocabulary files for the use in our visual analytics system NMTVis. There are models for German to English and vice versa available for an LSTM-based and the Transformer architecture.

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...

PN 6-6(Universität Stuttgart)

Sep 29, 2021

SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

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.

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Oct 15, 2021

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

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