We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We r...
Felix A. Wichmann, Arnulf B. A. Graf, Eero P. Simo...
In this paper, we address the problem of hallucinating a high resolution face given a low resolution input face. The problem is approached through sparse coding. To exploit the fa...
Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representati...
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
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 ...