We propose a generative model that codes the geometry and appearance of generic visual object categories as a loose hierarchy of parts, with probabilistic spatial relations linkin...
We propose a block-based transform optimization and associated image compression technique that exploits regularity along directional image singularities. Unlike established work,...
Osman Gokhan Sezer, Oztan Harmanci, Onur G. Gulery...
The recently proposed method for image compression based on multi-scale recurrent patterns, the MMP (Multidimensional Multiscale Parser) has been shown to perform well for a large...
Eddie B. L. Filho, Murilo B. de Carvalho, Eduardo ...
This paper presents a class of algorithms suitable for model reduction of distributed systems. Distributed systems are not suitable for treatment by standard model-reduction algor...
We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabele...
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin ...