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NIPS
2007
13 years 10 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
UBIMOB
2009
ACM
225views Management» more  UBIMOB 2009»
14 years 1 months ago
Historical data storage for large scale sensor networks
Wireless sensor networks are rapidly finding their way through a plethora of new applications like precision farming and forestry, with increasing network scale, system complexit...
Loïc Petit, Abdelhamid Nafaa, Raja Jurdak
BC
2002
193views more  BC 2002»
13 years 8 months ago
Resonant spatiotemporal learning in large random recurrent networks
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives...
Emmanuel Daucé, Mathias Quoy, Bernard Doyon
CIKM
2008
Springer
13 years 10 months ago
Classifying networked entities with modularity kernels
Statistical machine learning techniques for data classification usually assume that all entities are i.i.d. (independent and identically distributed). However, real-world entities...
Dell Zhang, Robert Mao
CISSE
2008
Springer
13 years 10 months ago
Network Topology Impact on Influence Spreading
-- Networks composed of large number of nodes interacting in structured ways such as power grids, communication networks, social networks or market networks are critical port of th...
Sasho Gramatikov, Dimitar Trajanov, Ljupco Kocarev...