The aim of compressed sensing is to recover attributes of sparse signals using very few measurements. Given an overall bit budget for quantization, this paper demonstrates that th...
Victoria Kostina, Marco F. Duarte, Sina Jafarpour,...
Competitive analysis is the established tool for measuring the output quality of algorithms that work in an online environment. Recently, the model of advice complexity has been in...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
The paper describes a scheme for detecting vehicles in images. The proposed method approximately models the unknown distribution of the images of vehicles by learning higher order...
A. N. Rajagopalan, Philippe Burlina, Rama Chellapp...
For the non-local denoising approach presented by Buades et al., remarkable denoising results are obtained at high expense of computational cost. In this paper, a new algorithm th...
Jin Wang, Yanwen Guo, Yiting Ying, Yanli Liu, Quns...