SimTech EXC 2075 Project Network 6 "Machine learning for simulation"
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MPEG-4 Video - 94.4 MB - MD5: 870a6bea2d5039fcdf5f88694e277244
Video demonstrating our neural machine translation system.
ZIP Archive - 2.1 MB - MD5: 45e317e218545d8d33aaa99f0bb3ffef
Screenshots of our neural machine translation system.
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.
ZIP Archive - 2.7 MB - MD5: b8a072d4e77d1306ec3e027a5537c2cc
Source code of our visual analytics system
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). The experiments from the accompanying paper (see below) are implemente...
R Syntax - 26.5 KB - MD5: 406e38f92fbf258e7b0dff76e476c307
Unknown - 508 B - MD5: efd4078885864b7ee481d7a750a7fc92
R Syntax - 2.0 KB - MD5: f118a5dd0ac9405176e3e031b22cfdba
Jupyter Notebook - 268.9 KB - MD5: 51ef505d165efcfb6df367a909152beb
Tabular Data - 63.7 KB - 5 Variables, 1000 Observations - UNF:6:0S3zNKpiYSHYquMApi4R5A==
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