CAREER: Provable Patching of Deep Neural Networks

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Deep neural networks (DNNs) have been successfully applied to a wide variety of problems, including image recognition, natural-language processing, medical diagnosis, and self-driving cars. As the accuracy of DNNs has increased so has their complexity and size. Moreover, DNNs are far from infallible, and mistakes made by DNNs have led to loss of life, motivating research on verification and testing to find mistakes in DNNs. In contrast, the central goal of this research is to develop techniques and tools for repairing a trained DNN once a mistake has been discovered. Provable Patching of DNNs computes a minimal change (patch) to the parameters of a trained DNN to correct its behavior according to a given specification. The project is interdisciplinary, combining the areas of Formal Methods and Machine Learning. The project develops theoretical foundations, designs efficient algorithms, and evaluates practical applications of Provable Patching of DNNs. The intellectual merits are (i) ensuring that the patching techniques are provably effective, generalizing, local, and efficient, and (ii) supporting different classes of safety and fairness specifications. The broader impacts of the project include (i) developing new undergraduate and graduate courses related to program correctness and formal methods, and (ii) broadening the participation of Computer Science undergraduate students by developing education and research activities targeting community college and transfer students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.