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DPPy: Sampling Determinantal Point Processes ...
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Document type :
Article dans une revue scientifique
Title :
DPPy: Sampling Determinantal Point Processes with Python
Author(s) :
Gautier, Guillaume [Auteur]
Sequential Learning [SEQUEL]
Bardenet, Remi [Auteur] refId
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Valko, Michal [Auteur] refId
Sequential Learning [SEQUEL]
Journal title :
Journal of Machine Learning Research
Publisher :
Microtome Publishing
Publication date :
2019
ISSN :
1532-4435
English keyword(s) :
determinantal point processes
sampling schemes
HAL domain(s) :
Statistiques [stat]/Machine Learning [stat.ML]
English abstract : [en]
Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. ...
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Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms. The project is hosted on GitHub and equipped with an extensive documentation. This documentation takes the form of a short survey of DPPs and relates each mathematical property with DPPy objects.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Inférence bayésienne à ressources limitées - données massives et modèles coûteux
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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