Fill-reducing sparse matrix orderings have been a topic of active research for many years. Although most such algorithms are developed and analyzed within a graph-theoretical frame...
Extracting the inductance of complex interconnect topologies is a formidable task, and simulating the resulting dense partial inductance matrix is even more difficult. Furthermore...
Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictiona...
Louise Benoit, Julien Mairal, Francis Bach, Jean P...
We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists...
We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machine (SVM)...