Learning Invariants through Soft Unification

TL;DR

This paper introduces Unification Networks, a neural approach that learns invariants by soft unification of examples, enabling machines to generalize patterns without human pre-engineering.

Contribution

It presents a novel end-to-end differentiable neural network architecture that learns invariants through soft unification, advancing machine reasoning capabilities.

Findings

Improves pattern recognition by learning invariants from data

Demonstrates effectiveness on five diverse datasets

Enhances generalization over baseline models

Abstract

Human reasoning involves recognising common underlying principles across many examples. The by-products of such reasoning are invariants that capture patterns such as "if someone went somewhere then they are there", expressed using variables "someone" and "somewhere" instead of mentioning specific people or places. Humans learn what variables are and how to use them at a young age. This paper explores whether machines can also learn and use variables solely from examples without requiring human pre-engineering. We propose Unification Networks, an end-to-end differentiable neural network approach capable of lifting examples into invariants and using those invariants to solve a given task. The core characteristic of our architecture is soft unification between examples that enables the network to generalise parts of the input into variables, thereby learning invariants. We evaluate our approach on five datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines.