Stochastic Gradient MCMC with Repulsive Forces
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We propose a unifying view of two different families of Bayesian inference algorithms, SG-MCMC and SVGD. We show that SVGD plus a noise term can be framed as a multiple chain SG-MCMC method. Instead of treating each parallel chain independently from others, the proposed algorithm implements a repulsive force between particles, avoiding collapse. Experiments in both synthetic distributions and real datasets show the benefits of the proposed scheme.
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