Prior Learning and Gibbs Reaction-Diffusion

Prior Learning and Gibbs Reaction-Diffusion

Prior Learning and Gibbs Reaction - Di usionSong

This article addresses two important themes in early visual computation: rst it presents a novel theory for learning the universal statistics of natural images { a prior model for typical cluttered scenes of the world { from a set of natural images, second it proposes a general framework of designing reaction-diiusion equations for image processing. We start by studying the statistics of natura...

Kinetics of Fe2O3-Al reaction prior to mechanochemical synthesis of Fe3Al-Al2O3 nanocomposite powder using thermal analysis

The effect of ball milling on kinetics of the thermite reaction of 3Fe2O3 + 8Al powder mixture to synthesizeFe3Al-Al2O3 nanocomposite was investigated using differential thermal analysis. A model-free methodwas applied to the non-isothermal differential calorimetry (DSC) data to evaluate the reaction kineticsaccording to the Starink method. The activation energy of the thermit...

Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion

We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses. This objective function, called the policy Gibbs error, is the expected error rate of a random classifier drawn from the prior distribution on the examples adaptively selected by the active learning policy. Exact maximization of the policy Gibbs error is hard, so we propose a greedy stra...

Unsupervised Bayesian Learning of HMM Transition Parameters by Moment Matching

Hidden Markov Models (HMMs) provide a simple framework to model the generative process of some types of sequential of data. We consider the challenging problem of estimating the parameters of an HMM incrementally by doing a constant amount of computation per observation. We propose an online moment matching algorithm for Bayesian learning of transition parameters of an HMM. While moment matchin...