Introduction
Neural network verification is becoming increasingly important as we deploy these systems in safety-critical applications. This lecture introduces the fundamental concepts and techniques used to formally verify the behavior of neural networks.
Mathematical Foundations
The mathematical foundation of neural network verification relies on concepts from formal methods, optimization theory, and computational logic. We'll explore how these areas intersect to provide guarantees about neural network behavior.
Key Concepts
- Formal specifications
- Satisfiability Modulo Theories (SMT)
- Linear programming relaxations
- Abstract interpretation
Verification Techniques
Several approaches exist for neural network verification, each with their own strengths and limitations...
Conclusion
Neural network verification is an active area of research with significant practical implications for the deployment of AI systems in critical applications.