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Showing 1 to 2 of 2 for “"Stein Variational Gradient Descent"”.

  1. Are Particle-Based Methods the Future of Sampling in Joint Energy Models? A Deep Dive into SVGD and SGLD

    This thesis investigates the integration of Stein Variational Gradient Descent (SVGD) with Joint Energy Models (JEMs), comparing its performance to Stochastic Gradient Langevin Dynamics (SGLD). We incorporated a generative loss term with an entropy component to enhance diversity and a smoothing …

    vt Repository record for Are Particle-Based Methods the Future of Sampling in Joint Energy Models? A Deep Dive into SVGD and SGLD (opens in a new tab)

  2. Variational Inference and Probabilistic Models for Parametric Partial Differential Equations

    … solving various problems relating to PDEs though variational inference and probabilistic models. The work is composed of three contributions. The first contribution lies in creating active learning surrogates for Bayesian inverse problems called Active learning projected surrogates – SVGD. Here we …

    cambridge Repository record for Variational Inference and Probabilistic Models for Parametric Partial Differential Equations (opens in a new tab)