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 language||American English|
|Number of pages||12|
|State||Published - 1 Jan 2019|
|Event||Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 - |
Duration: 1 Jan 2019 → …
|Conference||Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017|
|Period||1/01/19 → …|