# Learning Logistic Circuits

## 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.

## Key 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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Source: https://tomesphere.com/paper/1902.10798