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Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps

Reimers, Christian and Runge, Jakob and Denzler, Joachim (2020) Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps. In: Proceedings of the 9th International Workshop on Climate Informatics: CI 2019, pp. 1-4. 9th Int. Work. Clim. Informatics, Paris.

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Item URL in elib:https://elib.dlr.de/131637/
Document Type:Conference or Workshop Item (Lecture)
Title:Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Reimers, ChristianUNSPECIFIEDUNSPECIFIED
Runge, JakobUNSPECIFIEDUNSPECIFIED
Denzler, JoachimUNSPECIFIEDUNSPECIFIED
Date:7 January 2020
Journal or Publication Title:Proceedings of the 9th International Workshop on Climate Informatics: CI 2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-4
Status:Published
Keywords:climate informatics
Event Title:9th Int. Work. Clim. Informatics
Event Location:Paris
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Climate, Weather and Environment
Location: Jena
Institutes and Institutions:Institute of Data Science > Datamangagement and Analysis
Deposited By: Runge, Jakob
Deposited On:07 Jan 2020 13:28
Last Modified:07 Jan 2020 13:28

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