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Showing 1 to 3 of 3 for “"determinantal point process"”.
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Diversity-inducing probability measures for machine learning
… Perhaps the best known instance of a DIPM is a Determinantal Point Process (DPP). DPPs originally arose in quantum physics, and are known to have deep relations to linear algebra, combinatorics, and geometry. We explore applications of DPPs to kernel matrix approximation and kernel ridge …
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Advances in Stochastic Geometry for Cellular Networks
… are modeled as independent homogeneous Poisson point processes (PPPs). Despite its usefulness, the PPP-based network models fail to capture any spatial coupling between the users and BSs which is dominant in a multi-tier cellular network (also known as the heterogeneous cellular networks …
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Scalable Inference Algorithms for Determinantal Point Processes
Determinantal Point Processes (DPPs) are probability distributions on subsets of a collection of points that tend to generate diverse configurations of points. This feature makes them suitable as a probabilistic model of diversity. Recently this idea has been exploited extensively in subset …