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.