A Scalable Technique for Weak-Supervised Learning with Domain Constraints

TL;DR

This paper introduces a scalable end-to-end method that leverages symbolic domain knowledge as constraints to improve weakly supervised neural network training, especially for clustering-like data, demonstrating significant efficiency gains.

Contribution

The authors present a novel pipeline that reformulates domain constraints for efficient optimization, enabling scalable weak supervision in neural network training.

Findings

Outperforms previous methods in scalability

Effective on a variant of MNIST with sequence sum labels

Reduces computational complexity significantly

Abstract

We propose a novel scalable end-to-end pipeline that uses symbolic domain knowledge as constraints for learning a neural network for classifying unlabeled data in a weak-supervised manner. Our approach is particularly well-suited for settings where the data consists of distinct groups (classes) that lends itself to clustering-friendly representation learning and the domain constraints can be reformulated for use of efficient mathematical optimization techniques by considering multiple training examples at once. We evaluate our approach on a variant of the MNIST image classification problem where a training example consists of image sequences and the sum of the numbers represented by the sequences, and show that our approach scales significantly better than previous approaches that rely on computing all constraint satisfying combinations for each training example.