combining deep learning algorithms and logic circuits for biomedical data analysis
As a visiting researcher at the Krishnaswamy Lab at Yale University and in close collaboration with researchers from Google AI and the University of California, Berkeley, I worked on combining deep learning algorithms and logic circuits for biomedical data analysis. As part of the project, I developed a novel logic learning pipeline that makes predictions more interpretable and reduces hardware costs. This is beneficial in the context of patient care to enable risk stratification and clinical decision making.
Example: Translation of a single neuron to a logic circuit block.
Example: Translation of a decision tree to a logic circuit block.
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.
@article{brudermueller2020making,title={Making logic learnable with neural networks},author={Brudermueller, Tobias and Shung, Dennis L and Stanley, Adrian J and Stegmaier, Johannes and Krishnaswamy, Smita},journal={arXiv preprint arXiv:2002.03847},year={2020},}
