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ICML
2007
IEEE
16 years 3 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
ICML
2003
IEEE
16 years 3 months ago
Learning on the Test Data: Leveraging Unseen Features
This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and t...
Benjamin Taskar, Ming Fai Wong, Daphne Koller
POPL
2009
ACM
16 years 2 months ago
State-dependent representation independence
Mitchell's notion of representation independence is a particularly useful application of Reynolds' relational parametricity -- two different implementations of an abstra...
Amal Ahmed, Derek Dreyer, Andreas Rossberg
EMSOFT
2007
Springer
15 years 6 months ago
A unified practical approach to stochastic DVS scheduling
This paper deals with energy-aware real-time system scheduling using dynamic voltage scaling (DVS) for energy-constrained embedded systems that execute variable and unpredictable ...
Ruibin Xu, Rami G. Melhem, Daniel Mossé
ALT
2006
Springer
15 years 11 months ago
Learning Linearly Separable Languages
This paper presents a novel paradigm for learning languages that consists of mapping strings to an appropriate high-dimensional feature space and learning a separating hyperplane i...
Leonid Kontorovich, Corinna Cortes, Mehryar Mohri