Subject: Computer and Information Science
License: MIT License
Author Affiliation: Universität Stuttgart
1 to 5 of 5 Results
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-theo... |
May 27, 2024 - PN 6-4
Munz-Körner, Tanja; Weiskopf, Daniel, 2024, "Visual Analysis System to Explore the Visual Quality of Multidimensional Time Series Projections", https://doi.org/10.18419/darus-3553, DaRUS, V1
Source code of our visual analysis system for the exploration of the visual quality of multidimensional time series projections. This project contains source code for preprocessing data and the visual analysis system. Additionally, we added precomputed data for immediate use in t... |
Mar 14, 2024 - PN 6A-4
Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data", 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: "On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as follows: ├── analysis_results\
│ ├── plots\
├... |
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 |
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. |