Determinantal Point Processes for Machine Learning 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Determinantal Point Processes for Machine Learning

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Alex Kulesza 作者
Now Publishers Inc
譯者
2012 出版日期
178 頁數
0 價格
平裝
Foundations and Trends® in Machine Learning 叢書系列
9781601986283 圖書編碼

Determinantal Point Processes for Machine Learning 在線電子書 圖書標籤: Machine_Learning   


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Determinantal Point Processes for Machine Learning 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Determinantal Point Processes for Machine Learning 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Determinantal Point Processes for Machine Learning 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Determinantal Point Processes for Machine Learning 在線電子書 圖書描述

Alex Kulesza and Ben Taskar (2012), "Determinantal Point Processes for Machine Learning", Foundations and Trends® in Machine Learning: Vol. 5: No. 2–3, pp 123-286. http://dx.doi.org/10.1561/2200000044

https://www.nowpublishers.com/article/Details/MAL-044

http://www.alexkulesza.com/pubs/dpps_fnt12.pdf

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. While they have been studied extensively by mathematicians, giving rise to a deep and beautiful theory, DPPs are relatively new in machine learning. Determinantal Point Processes for Machine Learning provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and shows 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. It presents the general mathematical background to DPPs along with a range of modeling extensions, efficient algorithms, and theoretical results that aim to enable practical modeling and learning.

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