Divergence measures and a general framework for local variational approximation

A Relaxed Extra Gradient Approximation Method of Two Inverse-Strongly Monotone Mappings for a General System of Variational Inequalities, Fixed Point and Equilibrium Problems

Variational Inference for Bayesian Mixtures of Factor Analysers

We present an algorithm that infers the model structure of a mixture of factor analysers using an efficient and deterministic variational approximation to full Bayesian integration over model parameters. This procedure can automatically determine the optimal number of components and the local dimensionality of each component (Le. the number of factors in each factor analyser) . Alternatively it...

Alpha-Divergences in Variational Dropout

We investigate the use of alternative divergences to Kullback-Leibler (KL) in variational inference(VI), based on the Variational Dropout [10]. Stochastic gradient variational Bayes (SGVB) [9] is a general framework for estimating the evidence lower bound (ELBO) in Variational Bayes. In this work, we extend the SGVB estimator with using Alpha-Divergences, which are alternative to divergences to...

Variational Particle Approximations

Monte Carlo methods provide a powerful framework for approximating probability distributions with a set of stochastically sampled particles. In this paper, we rethink particle approximations from the perspective of variational inference, where the particles play the role of variational parameters. This leads to a deterministic version of Monte Carlo in which the particles are selected to optimi...

A robust variational approach for simultaneous smoothing and estimation of DTI

Estimating diffusion tensors is an essential step in many applications - such as diffusion tensor image (DTI) registration, segmentation and fiber tractography. Most of the methods proposed in the literature for this task are not simultaneously statistically robust and feature preserving techniques. In this paper, we propose a novel and robust variational framework for simultaneous smoothing an...