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» Explaining inferences in Bayesian networks
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AAAI
2008
13 years 10 months ago
Hybrid Markov Logic Networks
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
Jue Wang, Pedro Domingos
SRDS
2010
IEEE
13 years 5 months ago
Shedding Light on Enterprise Network Failures Using Spotlight
Abstract--Fault localization in enterprise networks is extremely challenging. A recent approach called Sherlock makes some headway into this problem by using an inference algorithm...
Dipu John, Pawan Prakash, Ramana Rao Kompella, Ran...
IDA
2010
Springer
13 years 9 months ago
Data Mining for Modeling Chiller Systems in Data Centers
We present a data mining approach to model the cooling infrastructure in data centers, particularly the chiller ensemble. These infrastructures are poorly understood due to the lac...
Debprakash Patnaik, Manish Marwah, Ratnesh K. Shar...
ACL
2007
13 years 9 months ago
Much ado about nothing: A social network model of Russian paradigmatic gaps
A number of Russian verbs lack 1sg nonpast forms. These paradigmatic gaps are puzzling because they seemingly contradict the highly productive nature of inflectional systems. We m...
Robert Daland, Andrea D. Sims, Janet Pierrehumbert
MINENET
2005
ACM
14 years 1 months ago
Shrink: a tool for failure diagnosis in IP networks
Faults in an IP network have various causes such as the failure of one or more routers at the IP layer, fiber-cuts, failure of physical elements at the optical layer, or extraneo...
Srikanth Kandula, Dina Katabi, Jean-Philippe Vasse...