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Titlebook: Handbook of Variational Methods for Nonlinear Geometric Data; Philipp Grohs,Martin Holler,Andreas Weinmann Book 2020 Springer Nature Switz

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Deep Variational Inferenceg an optimization problem for approximating the posterior. VI is scaled to stochastic variational inference and generalized to black-box variational inference (BBVI). Amortized VI leads to the variational auto-encoder (VAE) framework which is introduced using deep neural networks and graphical model
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Shape Analysis of Functional Datafaces, or trajectories on nonlinear manifolds. Rather than applying past statistical techniques developed using standard Hilbert norm, we focus on analyzing functions according to their shapes. We summarize recent developments in the field of elastic shape analysis of functional data, with a perspec
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Iddo Drorithat early stage of the new science. However, in vivo angiography was not achieved until the 1920s, and it needed the development of safer intravascular contrast media in the 1930s, and percutaneous techniques of injection in the 1940s and 1950s for percutaneous arteriography to become a routine radiological procedure.
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