Oct 20, 2022 · The mean field variational inference (MFVI) formulation restricts the general Bayesian inference problem to the subspace of product measures. We ...
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We obtain representations of Mean-Field Variational Inference (MFVI) in the forms of gradient flows on product spaces, quasilinear partial differential ...
Dec 10, 2023 · We obtain representations of Mean-Field Variational Inference (MFVI) in the forms of gradient flows on product spaces, quasilinear partial ...
Poster in. Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences. On Representations of Mean-Field Variational Inference.
Here we focus on using mean field algorithm for approximate inference. When we are doing the mean field approximation, we assume the variational approximation q ...
How do we optimize the ELBO in mean field variational inference? ○ Typically, we use coordinate ascent optimization. ○ I.e. we optimize each latent variable's ...
Nov 27, 2020 · Variational Inference: Mean Field, Normalizing Flows and beyond. Maxim Panov based on the joint work with A. Thin, N. Kotelevskii, A. Durmus and ...
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Jun 16, 2020 · Report a summary, e.g. posterior means and (co)variances. Inform your boss/client about the 'insights' provided by the model, i.e. “on average ...
2 Mean field approximation; 3 A duality formula for variational inference; 4 A basic example. 4.1 The mathematical model; 4.2 The joint probability; 4.3 ...
Dec 5, 2023 · Our main application is to the problem of mean-field variational inference, which seeks to approximate a distribution \pi over \mathbb{R}^d by a ...
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