FSPN is a new class of probabilistic graphical model (PGM) It can simultaneously attain the two desirable goals: high estimationaccuracy and fast inference speed . Bayesian network (BN) has low inference efficiency and sum product network performance significantly degrades in presence of highly correlated variables . We present efficient probability inferenceand structure learning algorithms for FSPn, along with theoretical analysis and theoretical analysis . Our experimental results on the benchmarkdatasets indicate that FSPNs is a . new SOTA PGM .

Author(s) : Ziniu Wu, Rong Zhu, Andreas Pfadler, Yuxing Han, Jiangneng Li, Zhengping Qian, Kai Zeng, Jingren Zhou

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project




Keywords : fspn - theoretical - network - class - probabilistic -

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