SELF-SUPERVISED LEARNING METHODS FOR REPRESENTATION LEARNING AND ANOMALY DETECTION IN TIME SERIES: APPLICATIONS TO ASTRONOMY AND ELECTROENCEPHALOGRAPHY
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Fecha de publicación: | 2022 |
Objetivos: | The main objectives of this research proposal are to develop an information theoretic framework for self-supervised learning based methods for representation learning and anomaly detection in time-series, applied to astronomical light curves and electroencephalograms |
Instrumento: | FONDECYT |
Año de Inicio/Término: | 2022-2026 |
Financiamiento/Sponsor: | CONICYT |
Rol del Usuario: | INVESTIGADOR(A) RESPONSABLE |
DOI: |
1220829 |