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

页码：601 / 604

页数：4

共 50 条

[1] InferPy: Probabilistic modeling with deep neural networks made easy
Cozar, Javier
Cabanas, Rafael
Salmeron, Antonio
Masegosa, Andres R.
[J]. NEUROCOMPUTING, 2020, 415 : 408 - 410
[2] Inference in Probabilistic Graphical Models by Graph Neural Networks
Yoon, KiJung
Liao, Renjie
Xiong, Yuwen
Zhang, Lisa
Fetaya, Ethan
Urtasun, Raquel
Zemel, Richard
Pitkow, Xaq
[J]. CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, 2019, : 868 - 875
[3] Algorithmic Music Composition Using Probabilistic Graphical Models and Artificial Neural Networks
Marsden, Marc
Ajoodha, Ritesh
[J]. 2021 SOUTHERN AFRICAN UNIVERSITIES POWER ENGINEERING CONFERENCE/ROBOTICS AND MECHATRONICS/PATTERN RECOGNITION ASSOCIATION OF SOUTH AFRICA (SAUPEC/ROBMECH/PRASA), 2021,
[4] Probabilistic Graphical Models Applied to Biological Networks
Murad, Natalia Faraj
Brandao, Marcelo Mendes
[J]. ADVANCES IN PLANT OMICS AND SYSTEMS BIOLOGY APPROACHES, 2021, 1346 : 119 - 130
[5] CLASSIFICATION OF MULTIMISSION SAR IMAGES BASED ON PROBABILISTIC GRAPHICAL MODELS AND CONVOLUTIONAL NEURAL NETWORKS
Pastorino, Martina
Moser, Gabriele
Serpico, Sebastiano B.
Zerubia, Josiane
[J]. IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 1420 - 1423
[6] On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks
Baldi, P
Rosen-Zvi, M
[J]. NEURAL NETWORKS, 2005, 18 (08) : 1080 - 1086
[7] Inferring cellular networks using probabilistic graphical models
Friedman, N
[J]. SCIENCE, 2004, 303 (5659) : 799 - 805
[8] Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification
Pastorino, Martina
Moser, Gabriele
Serpico, Sebastiano B.
Zerubia, Josiane
[J]. 29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021), 2021, : 1740 - 1744
[9] Learning of Discrete Graphical Models with Neural Networks
Abhijith, J.
Lokhov, Andrey Y.
Misra, Sidhant
Vuffray, Marc
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
[10] Probabilistic Models with Deep Neural Networks
Masegosa, Andres R.
Cabanas, Rafael
Langseth, Helge
Nielsen, Thomas D.
Salmeron, Antonio
[J]. ENTROPY, 2021, 23 (01) : 1 - 27

← 1 2 3 4 5 →