A Scalable Technique for Weak-Supervised Learning with Domain Constraints
This addresses scalability issues in weak-supervised learning with domain constraints, though it appears incremental as it builds on existing constraint-based methods.
The authors tackled the problem of weak-supervised learning with domain constraints by proposing a scalable pipeline that uses symbolic knowledge as constraints for neural network classification, showing it scales significantly better than previous approaches on a variant of the MNIST image classification problem.
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.