The intended audience is computer science or math students with background in logic and algorithms.
We assume familiarity with algorithms and logic. While basic knowledge of (deep) neural networks is helpful, in-depth familiarity with deep learning is not required. Please contact the instructor should you be unsure if you have the necessary background.
The exceptional performance of deep neural networks in areas such as perception and natural language processing has made them an integral part of many real-world AI systems, including safety-critical ones such as medical diagnosis and autonomous driving. However, neural networks are inherently opaque, and numerous defects have been found in state-of-the-art networks.
In this seminar, we will study various methods for proving the reliability of deep neural networks. To this end, we will work with the book "Introduction to Neural Network Verification" by Aws Albarghouthi and select current research papers. Please note that this seminar will focus on formal methods, including topics related to logic and automated reasoning. It is not about deep learning.