Determinantal point processes for machine learning
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Publication:2871586
DOI10.1561/2200000044zbMATH Open1278.68240arXiv1207.6083OpenAlexW2138779671MaRDI QIDQ2871586
Author name not available (Why is that?)
Publication date: 8 January 2014
Published in: (Search for Journal in Brave)
Abstract: Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and show how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling non-overlapping human poses in images or video, and automatically building timelines of important news stories.
Full work available at URL: https://arxiv.org/abs/1207.6083
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