We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. denite clause programs containing probabilistic facts with a ...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
—With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as w...
Jiangtian Li, Xiaosong Ma, Karan Singh, Martin Sch...
In this paper, we introduce a tuned eigenspace technique so as to classify human motion. The method presented here overcomes those problems related to articulated motion and dress...
This paper presents a mutation-based evolutionary algorithm that evolves genotypic genes for regulating developmental timing of phenotypic values. The genotype sequentially genera...