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From GPLVM to Deep GPs
César Lincoln Mattos

Mon Dec 06 12:05 PM -- 12:27 PM (PST) @

We consider the unsupervised setting, more specifically, the task of nonlinear dimensionality reduction. We summarize the GP latent variable model (GPLVM) and indicate how it can be used as a building block for other generative models, such as the deep GP and other flexible probabilistic models.

Author Information

César Lincoln Mattos (Federal University of Ceará)

César Lincoln Cavalcante Mattos is an associate professor at the Department of Computer Science, at Federal University of Ceará (UFC), Brazil. He is also an associate researcher at the Logics and Artificial Intelligence Group (LOGIA). He has research interests in the broad fields of machine learning and probabilistic modeling, such as Gaussian processes, deep (probabilistic) learning, approximate inference and system identification. He has been applying learning methods in several research and development collaborations in areas such as dynamical system modeling, health risk analysis, software repository mining and anomaly detection.

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