  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.
