This paper proposes an image coding method based on adaptive downsampling which not only uses the pixel redundancy but also considers visual redundancy. At the encoder side, codec...
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications. We discuss two re...
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
An active e-course is a self-representable and self-organizable document mechanism with a flexible structure. The kernel of the active e-course is to organize learning materials i...
In limited data domains, many effective language modeling techniques construct models with parameters to be estimated on an in-domain development set. However, in some domains, no...