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...
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...
Mitchell's notion of representation independence is a particularly useful application of Reynolds' relational parametricity -- two different implementations of an abstra...
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 ...
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...