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Maison de la Modélisation, de la Simulation et des Interactions


Marco Lorenzi's Seminar: Deep Neural Networks and Gaussian Processes for temporal analysis of clinical data

The MSI kindly invites you to attend the following seminar run by Marco Lorenzi in the MSI's premises in Sophia Antipolis on Thursday, April 18 at 10:00


18/04/2019   :   10h00
1361 Route des Lucioles, 06560 Valbonne, France
Publication : 18/04/2019
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Deep Neural Networks and Gaussian Processes for temporal analysis of clinical data

Abstract: Deep Neural Networks (DNN) and  Gaussian Processes (GP) are modern machine learning methods that have been shown to outperform standard analysis approaches in many applications. These approaches come with theoretical guarantees of being able to approximate any function, thus providing high flexibility and expressivity in inferring complex data relationships. Furthermore, recent methodological developments have allowed to reformulate these learning frameworks to efficiently account for uncertainty quantification in complex and noisy data. This talk will focus on the recent advancements in GP and probabilistic DNN modeling, and will illustrate novel applications of these methodologies to the analysis of biomedical data. In particular, we will provide examples of spatio-temporal modeling and source separation of high-dimensional brain imaging data, disease progression modeling through time warping of time-series, and bio-mechanical modeling via derivative-constrained GP.

Please register via the following web page: http://univ-cotedazur.fr/events/msi-seminars

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