Making Logic Learnable With Neural Networks
This work addresses the need for interpretable and verifiable AI systems in domains like biomedical data analysis, though it appears incremental as it builds on existing translation methods.
The authors tackled the problem of combining the learning capabilities of neural networks with the hardware implementability and verifiability of logic circuits, achieving greater accuracy and reduced hardware cost compared to naive translations.
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally verifiable. On the other hand, logic circuits are implementable, verifiable, and interpretable but are not able to learn from training data in a generalizable way. We propose a novel logic learning pipeline that combines the advantages of neural networks and logic circuits. Our pipeline first trains a neural network on a classification task, and then translates this, first to random forests, and then to AND-Inverter logic. We show that our pipeline maintains greater accuracy than naive translations to logic, and minimizes the logic such that it is more interpretable and has decreased hardware cost. We show the utility of our pipeline on a network that is trained on biomedical data. This approach could be applied to patient care to provide risk stratification and guide clinical decision-making.