Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the ...
Mark W. Schmidt, Alexandru Niculescu-Mizil, Kevin ...
We address the problem of suggesting who to add as an additional recipient (i.e. cc, or carbon copy) for an email under composition. We address the problem using graphical models ...
In this paper, we position the correct way of using graphical models for enhancing cyber security analysis in enterprise networks. Graphical models can be powerful in representati...
In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selectin...
Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in...
Accurate timing analysis is key to efficient embedded system synthesis and integration. While industrial control software systems are developed using graphical models, such as Ma...
Jan Staschulat, Rolf Ernst, Andreas Schulze, Fabia...
Abstract. We introduce a new framework for feature grouping based on factor graphs, which are graphical models that encode interactions among arbitrary numbers of random variables....
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...
We consider the problem of learning multiscale graphical models. Given a collection of variables along with covariance specifications for these variables, we introduce hidden var...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to...
Elena Zheleva, John Guiver, Eduarda Mendes Rodrigu...