: Recurrent neural networks possess interesting universal approximation capabilities, making them good candidates for time series modeling. Unfortunately, long term dependencies ar...
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
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...
The classical Douglas-Peucker line-simplification algorithm is recognized as the one that delivers the best perceptual representations of the original lines. It is used extensivel...
Scatterometers have been launched primarily to measure ocean winds. The value of scatterometer data is increased by application of the SIR (Scatterometer Image Reconstruction) alg...