dc.contributor.author	Marshall, Carl S.	en_US
dc.contributor.author	Vembar, Deepak S.	en_US
dc.contributor.author	Ganguly, Sujoy	en_US
dc.contributor.author	Guinier, Florent	en_US
dc.contributor.editor	Hahmann, Stefanie	en_US
dc.contributor.editor	Patow, Gustavo A.	en_US
dc.date.accessioned	2022-04-22T11:46:01Z
dc.date.available	2022-04-22T11:46:01Z
dc.date.issued	2022
dc.description.abstract	Applying machine learning to improve graphics rendering or asset pipelines is challenging. Practicalities such as proprietary datasets, network retraining, and deployment issues make it difficult to translate published research into deployed solutions. In this course, industry practitioners at the forefront of this interdisciplinary field discuss and outline potential solutions.	en_US
dc.description.sectionheaders	Tutorials
dc.description.seriesinformation	Eurographics 2022 - Tutorials
dc.identifier.doi	10.2312/egt.20221059
dc.identifier.isbn	978-3-03868-172-4
dc.identifier.issn	1017-4656
dc.identifier.uri	https://doi.org/10.2312/egt.20221059
dc.identifier.uri	https://diglib.eg.org:443/handle/10.2312/egt20221059
dc.publisher	The Eurographics Association	en_US
dc.rights	Attribution 4.0 International License
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.title	Practical Machine Learning for Rendering: From Research to Deployment	en_US

Practical Machine Learning for Rendering: From Research to Deployment