We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
Along with the popularity of software-intensive systems, the interactions between system components and between humans and software applications are becoming more and more complex...
Computer-aided diagnosis is often based on comparing a structure of interest with prior models. Such a comparison requires automatic techniques in determining prior models from a s...
Xiaolei Huang, Nikos Paragios, Dimitris N. Metaxas
Background: Structure-based computational methods are needed to help identify and characterize protein-protein complexes and their function. For individual proteins, the most succ...