Mean Field Approximation for Fields of Experts

Mean Field Approximation for Fields of Experts

Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework

We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logar...

Online Mean Field Approximation for Automated Experimentation

In this paper, we propose a semi-supervised online graph labelling method that a↵ords early learning capability. We use mean field approximation for predicting the unknown labels of the vertices of the graph with high accuracy on the standard benchmark datasets. The minimum cut is the energy function of our probabilistic model that encodes the uncertainty about the labels of the vertices. Our m...

Estimation of Software Reliability by Sequential Testing with Simulated Annealing of Mean Field Approximation

Various problems of combinatorial optimization and permutation can be solved with neural network optimization. The problem of estimating the software reliability can be solved with the optimization of failed components to its minimum value. Various solutions of the problem of estimating the software reliability have been given. These solutions are exact and heuristic, but all the exact approach...

Information Geometry of Mean-Field Approximation

I present a general theory of mean-field approximation based on information geometry and applicable not only to Boltzmann machines but also to wider classes of statistical models. Using perturbation expansion of the Kullback divergence (or Plefka expansion in statistical physics), a formulation of mean-field approximation of general orders is derived. It includes in a natural way the "naive" me...