— We propose a modeling framework based on the event-driven paradigm for populations of neurons which interchange messages. Unlike other strategies our approach is focused on the...
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image ...
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inferen...
Mert R. Sabuncu, B. T. Thomas Yeo, Koenraad Van Le...
The design of large-scale, distributed, performance-sensitive systems presents numerous challenges due to their networkcentric nature and stringent quality of service (QoS) requir...
Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Ni~no Southern Oscill...