Credal sum-product networks

Denis Deratani Mauá, Fabio Gagliardi Cozman, Diarmaid Conaty, Cassio Polpo De Campos

Research output: Contribution to conferenceConference Paper

5 Scopus citations

Abstract

Copyright © PMLR 2017. All rights reserved. Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic graphical models that allow for marginal inference with polynomial effort. As with other probabilistic models, sum-product networks are often learned from data and used to perform classification. Hence, their results are prone to be unreliable and overconfident. In this work, we develop credal sum-product networks, an imprecise extension of sum-product networks. We present algorithms and complexity results for common inference tasks. We apply our algorithms on realistic classification task using images of digits and show that credal sum-product networks obtained by a perturbation of the parameters of learned sum-product networks are able to distinguish between reliable and unreliable classifications with high accuracy.
Original languageAmerican English
Pages205-216
Number of pages12
StatePublished - 1 Jan 2019
Externally publishedYes
EventProceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 -
Duration: 1 Jan 2019 → …

Conference

ConferenceProceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017
Period1/01/19 → …

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