Probabilistic Graphical Models with Neural Networks in InferPy

被引：0

作者：

Cabanas, Rafael ^{[1
]}

Cozar, Javier ^{[2
,3
]}

Salmeron, Antonio ^{[2
,3
]}

Masegosa, Andres R. ^{[2
,3
]}

机构：

[1] Ist Dalle Molle Studi Intelligenza Artificiale ID, Lugano, Switzerland

[2] Univ Almeria, Dept Math, Almeria, Spain

[3] Univ Almeria, Ctr Dev & Transfer Math Res Ind CDTIME, Almeria, Spain

来源：

INTERNATIONAL CONFERENCE ON PROBABILISTIC GRAPHICAL MODELS, VOL 138 | 2020年 / 138卷

关键词：

Deep probabilistic modeling; Hierarchical probabilistic models; Variational Inference; Bayesian learning; TensorFlow; Keras; User-friendly;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

InferPy is an open-source Python package for variational inference in probabilistic models containing neural networks. Other similar libraries are often difficult for non-expert users. InferPy provides a much more compact and simple way to code such models, at the expense of slightly reducing expressibility and flexibility. The main objective of this package is to permit its use without having a strong theoretical background or thorough knowledge of the deep learning frameworks.

引用

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页码：601 / 604

页数：4

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