Verification of Neural Networks (Lecture Notes)

2026-04-28

AI Summary

This work addresses the lack of formal correctness guarantees for neural networks in safety-critical applications by proposing a unified formal verification framework applicable to diverse architectures, including feedforward networks, recurrent networks, and Transformers. The framework integrates expressive specification languages—such as linear temporal logic—with advanced algorithmic techniques, including abstract interpretation and SMT solving, to uniformly capture both semantic representations and verification methodologies. By generalizing existing verification theories to broader classes of models, this study not only extends the theoretical foundations of neural network verification but also establishes a scalable and rigorous basis for safety analysis of complex architectures. Consequently, it advances both the theoretical understanding and practical deployment of trustworthy artificial intelligence systems.

Abstract

These lecture notes provide an introduction to the verification of neural networks from a theoretical perspective. We discuss feed-forward neural networks, recurrent neural networks, attention mechanisms, and transformers, together with specification languages and algorithmic verification techniques.

Problem

• Neural Network Verification
• Formal Verification
• Specification Languages
• Transformers
• Recurrent Neural Networks

Innovation

• Neural Network Verification
• Formal Specification
• Algorithmic Verification
• Transformers
• Attention Mechanisms