Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of comp...
: Today, electronic cross-company collaboration is about to gain significant momentum, but still shows weaknesses with respect to productivity, flexibility and quality: A lack of s...
Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
Abstract—In this paper, we present the analysis and experimental validation of a vision-aided inertial navigation algorithm for planetary landing applications. The system employs...
Anastasios I. Mourikis, Nikolas Trawny, Stergios I...
In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose...