Determinantal Point Processes for Machine Learning

Determinantal Point Processes for Machine Learning
Author: Alex Kulesza
Publisher:
Total Pages: 176
Release: 2012
Genre: Computer algorithms
ISBN: 9781601986290


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This monograph provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community.


Determinantal Point Processes for Machine Learning
Language: en
Pages: 176
Authors: Alex Kulesza
Categories: Computer algorithms
Type: BOOK - Published: 2012 - Publisher:

GET EBOOK

This monograph provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine lear
Determinantal Point Processes for Machine Learning
Language: en
Pages: 178
Authors: Alex Kulesza
Categories: Computers
Type: BOOK - Published: 2012-11-29 - Publisher: Now Pub

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This monograph provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine lear
Learning with Determinantal Point Processes
Language: en
Pages: 178
Authors: John A. Kulesza
Categories:
Type: BOOK - Published: 2012 - Publisher:

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Learning and Enforcing Diversity with Determinantal Point Processes
Language: en
Pages: 66
Authors: Zelda Elaine Mariet
Categories:
Type: BOOK - Published: 2016 - Publisher:

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As machine-learning techniques continue to require more data and become increasingly memory-heavy, being able to choose a subset of relevant, high-quality and d
Zeros of Gaussian Analytic Functions and Determinantal Point Processes
Language: en
Pages: 170
Authors: John Ben Hough
Categories: Mathematics
Type: BOOK - Published: 2009 - Publisher: American Mathematical Soc.

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Examines in some depth two important classes of point processes, determinantal processes and 'Gaussian zeros', i.e., zeros of random analytic functions with Gau