The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian proces...
Ryan Prescott Adams, Iain Murray, David J. C. MacK...
This work exploits several machine-learning techniques to address the problem of image-quality prediction of synthetic aperture sonar (SAS) imagery. The objective is to predict th...
We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weights of a two-component noise model augments the standard process over laten...
This paper deals with the problem of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency d...
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...