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• W2964318004 abstract "This paper introduces the variational Renyi bound (VR) that extends traditional variational inference to Renyi's α-divergences. This new family of variational methods unifies a number of existing approaches, and enables a smooth interpolation from the evidence lower-bound to the log (marginal) likelihood that is controlled by the value of α that parametrises the divergence. The reparameterization trick, Monte Carlo approximation and stochastic optimisation methods are deployed to obtain a tractable and unified framework for optimisation. We further consider negative α values and propose a novel variational inference method as a new special case in the proposed framework. Experiments on Bayesian neural networks and variational auto-encoders demonstrate the wide applicability of the VR bound." @default.
• W2964318004 created "2019-07-30" @default.
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• W2964318004 date "2016-12-05" @default.
• W2964318004 modified "2023-10-04" @default.
• W2964318004 title "Rényi divergence variational inference" @default.
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• W2964318004 doi "https://doi.org/10.17863/cam.21344" @default.
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