Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research ...
This paper presents a framework for directly addressing issues arising from self-occlusions and ambiguities due to the lack of depth information in vector-based representations. V...
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the E...
Consider yourself faced with learning about a new system. You have lots of measurements available, but you really don't know which measurements affect the values of others. H...
In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by manyresearchers. Our analysis assumes a con...