# Learning with Partially Ordered Representations

arXiv: https://arxiv.org/abs/1906.07886 · HTML: https://mlsanity.com/paper/1906.07886/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Approximating Permutations with Neural Network Components for Travelling Photographer Problem](https://mlsanity.com/paper/2205.00242/) (2022)

## Abstract (arXiv)

This paper examines the characterization and learning of grammars defined with enriched representational models. Model-theoretic approaches to formal language theory traditionally assume that each position in a string belongs to exactly one unary relation. We consider unconventional string models where positions can have multiple, shared properties, which are arguably useful in many applications. We show the structures given by these models are partially ordered, and present a learning algorithm that exploits this ordering relation to effectively prune the hypothesis space. We prove this learning algorithm, which takes positive examples as input, finds the most general grammar which covers the data.