Individualizing deep dynamic models for psychological resilience data

Individualizing deep dynamic models for psychological resilience data

Abstract Deep learning approaches can uncover complex patterns in data. In particular, variational autoencoders achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project, which features in...

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Bibliographic Details
Main Authors: Göran Köber, Shakoor Pooseh, Haakon Engen, Andrea Chmitorz, Miriam Kampa, Anita Schick, Alexandra Sebastian, Oliver Tüscher, Michèle Wessa, Kenneth S. L. Yuen, Henrik Walter, Raffael Kalisch, Jens Timmer, Harald Binder
Format: Article
Language:English
Published: Nature Portfolio 2022-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-11650-6

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https://doi.org/10.1038/s41598-022-11650-6

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