This paper proposes a new classification model called logistic circuits. On MNIST and Fashion datasets, our learning algorithm outperforms neural networks that have an order of magnitude more parameters. Yet, logistic circuits have a distinct origin in symbolic AI, forming a discriminative counterpart to probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that parameter learning for logistic circuits is convex optimization, and that a simple local search algorithm can induce strong model structures from data.

@inproceedings{LiangTPM19,
  author    = {Liang, Yitao and Van den Broeck, Guy},
  title     = {Learning Logistic Circuits},
  booktitle = {Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM)},
  month     = 6,
  year      = {2019},
  url       = "http://starai.cs.ucla.edu/papers/LiangAAAI19.pdf",
  code = "https://github.com/UCLA-StarAI/LogisticCircuit",
  keywords  = {workshop,duplicate}
}