TL;DR
This paper introduces logistic circuits, a new classification model that outperforms larger neural networks on MNIST and Fashion datasets, combining symbolic AI origins with convex parameter learning.
Contribution
It presents logistic circuits as a novel discriminative model with convex parameter learning and a local search algorithm for structure induction.
Findings
Outperforms neural networks with more parameters on MNIST and Fashion datasets
Parameter learning is convex optimization
Simple local search induces effective model structures
Abstract
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.
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