— We propose a method that takes observations of a random vector as input, and learns to segment each observation into two disjoint parts. We show how to use the internal coheren...
During pseudorandom testing, a significant amount of energy and test application time is wasted for generating and for applying “useless” test vectors that do not contribute t...
Sheng Zhang, Sharad C. Seth, Bhargab B. Bhattachar...
This paper investigates the application of randomized algorithms for large scale SVM learning. The key contribution of the paper is to show that, by using ideas random projections...
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valu...
Marshall F. Tappen, Kegan G. G. Samuel, Craig V. D...