Classification problems with a very large or unbounded set of output categories are common in many areas such as natural language and image processing. In order to improve accurac...
Ivan Titov, Alexandre Klementiev, Kevin Small, Dan...
—Modeling natural and artificial systems has played a key role in various applications and has long been a task that has drawn enormous efforts. In this work, instead of explori...
Ronen Talmon, Dan Kushnir, Ronald R. Coifman, Isra...
Given observed data and a collection of parameterized candidate models, a 1- confidence region in parameter space provides useful insight as to those models which are a good fit t...
Brent Bryan, H. Brendan McMahan, Chad M. Schafer, ...
We present an efficient implementation of an approximate balanced truncation model reduction technique for general large-scale RLC systems, described by a statespace model where t...
—Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-compute...