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Non-Linear Temporal Machine Learning Models for Conditioning Monitoring in Large-Scale Solar Energy Systems
Maaløe, Lars
(PhD Student)
Winther, Ole
(Main Supervisor)
Nielsen, Ole Niels
(Supervisor)
Hauberg, Søren
(Examiner)
Paquet, Ulrich
(Examiner)
Turner, Richard
(Examiner)
Overview
Fingerprint
Publications
(1)
Project Details
Status
Finished
Effective start/end date
15/12/2014
→
13/06/2018
View all
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Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Machine Learning
Computer Science
100%
Generative Model
Computer Science
100%
Learning
Biochemistry, Genetics and Molecular Biology
100%
Supervised Machine Learning
Biochemistry, Genetics and Molecular Biology
100%
Unlabeled Data
Computer Science
50%
Deep Neural Network
Computer Science
33%
Models
Computer Science
33%
Development
Biochemistry, Genetics and Molecular Biology
33%
Research output
Publications per year
2018
2018
2018
1
Ph.D. thesis
Publications per year
Publications per year
Deep Generative Models for Semi-Supervised Machine Learning
Maaløe, L.
,
2018
, Kgs. Lyngby, Denmark:
DTU Compute
.
155 p.
(DTU Compute PHD-2018, Vol. 472).
Research output
:
Book/Report
›
Ph.D. thesis
Open Access
File
Machine Learning
100%
Generative Model
100%
Supervised Machine Learning
100%
Learning
100%
Unlabeled Data
50%
557
Downloads (Pure)