Graph-based representations play a key role in machine learning. The fundamental step in these representations is the association of a graph structure to a dataset. In this paper, we propose a method that finds a block sparse representation of the data by associating a graph, whose Laplacian matrix admits the sparsifying dictionary as its eigenvectors.
Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming.
Title: Machine Learning/Speech research internship @ Seattle, WA
Time: Summer/Fall 2019
About You
A wiliness to be a better you on a day by day basis.
About Us:
We enable hundreds of millions of commercial and social interactions among our users, between consumers and merchants, and among businesses every day though speech.
What You’ll Do
In recent years the ubiquity of mobile computing platforms such as smartphones and tablet devices has rapidly increased. These devices provide a range of interaction in an untethered environment unimaginable a decade previously. With this ability to interact with services and individuals, comes the need to accurately authenticate the identity of the person requesting the transaction, many of which may carry financial or legally-binding instruction.