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Current Trends on Kernel Design
César Lincoln Mattos

Mon Dec 06 11:52 AM -- 12:05 PM (PST) @

We highlight some recent advances to kernel design that enhance the use of GP models in complex data settings. From automatic composition of simple kernels to deep kernel learning and (graph) convolutional kernels, the works revised in this section indicate how fast-paced the research on kernel design is.

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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