DagRep.9.11.24.pdf
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Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable (Dagstuhl Seminar 19452)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 9, Issue 11
Volume: Dagstuhl Reports, Volume 9
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2020-03-31
Cite As Get BibTex
Enrico Bertini, Peer-Timo Bremer, Daniela Oelke, and Jayaraman Thiagarajan. Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable (Dagstuhl Seminar 19452). In Dagstuhl Reports, Volume 9, Issue 11, pp. 24-33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/DagRep.9.11.24
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 19452 "Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable".
Subject Classification
ACM Subject Classification
- Human-centered computing → Visualization
- Computing methodologies → Artificial intelligence
- Computing methodologies → Machine learning
Keywords
- Visualization
- Machine Learning
- Interpretability
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