Masatran, Rajasekaran

IIT MadrasYahooBTech, IIIT Hyderabad.

A Latent-Variable Lattice Model

Abstract: Markov random field (MRF) learning is intractable, and its approximation algorithms are computationally expensive. We target a small subset of MRF that is used frequently in computer vision. We characterize this subset with three concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as an alternative. Our goal is robust learning from small datasets. Our learning algorithm uses vector quantization and, at time complexity O(u log(u)) for a dataset of u pixels, is much faster than that of general-purpose MRF.

A Marginal-Based Technique for Distribution Estimation

Abstract: We propose a novel technique that estimates a distribution efficiently, using one-dimensional marginals. We apply a linear regression model on naive bayes, with the components being log-marginal probabilities. Compared to naive bayes, our technique provides better accuracy, without increase in computational complexity.