Compressive sensing (CS) exploits the sparsity present in many common signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, ...
Mark A. Davenport, Jason N. Laska, John R. Treichl...
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Dimension attributes in data warehouses are typically hierarchical (e.g., geographic locations in sales data, URLs in Web traffic logs). OLAP tools are used to summarize the measu...
We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-d...
We present a new test generation procedure for sequential circuits using newly traversed state and newly detected fault information obtained between successive iterations of vecto...
Ashish Giani, Shuo Sheng, Michael S. Hsiao, Vishwa...