Learning Logistic Circuits
Machine Learning
2019-03-01 v1 Artificial Intelligence
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.
Keywords
Cite
@article{arxiv.1902.10798,
title = {Learning Logistic Circuits},
author = {Yitao Liang and Guy Van den Broeck},
journal= {arXiv preprint arXiv:1902.10798},
year = {2019}
}
Comments
Published in the Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI19)