This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
The lexical acquisition system presented in this paper incrementallyupdates linguisticproperties of unknown words inferred from their surrounding context by parsing sentences with...
We present the results of our investigation into the use of Genetic Algorithms (GAs) for identifying near optimal design parameters of diagnostic systems that are based on Artifici...
High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible ...
Sahand Negahban, Pradeep Ravikumar, Martin J. Wain...