Mixed Formal Learning: A Path to Transparent Machine Learning

Sandra Carrico

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arXiv · 1901.06622

Mixed Formal Learning: A Path to Transparent Machine Learning

Sandra Carrico

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Abstract

This paper presents Mixed Formal Learning, a new architecture that learns models based on formal mathematical representations of the domain of interest and exposes latent variables. The second element in the architecture learns a particular skill, typically by using traditional prediction or classification mechanisms. Our key findings include that this architecture: (1) Facilitates transparency by exposing key latent variables based on a learned mathematical model; (2) Enables Low Shot and Zero Shot training of machine learning without sacrificing accuracy or recall.

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Sandra Carrico. 2019-01-20. Mixed Formal Learning: A Path to Transparent Machine Learning. https://arxiv.org/abs/1901.06622

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