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insight - Neural Network Verification
DEEPCDCL: A Novel Neural Network Verification Framework Based on CDCL Algorithm
DEEPCDCL introduces an efficient neural network verification framework based on the Conflict-Driven Clause Learning (CDCL) algorithm, aiming to enhance speed and accuracy in verification processes.
The Undecidability of the Reachability Problem for Neural-Network Control Systems
Determining whether a neural-network control system can reach a specific state from a set of initial states is undecidable, even for simple systems.
Differential Verification for Neural Network Equivalence: A Zonotope-based Approach with Confidence Guarantees
This paper proposes a novel approach to verifying the equivalence of neural networks, particularly after pruning and retraining, using differential verification with Zonotopes and introducing a new confidence-based equivalence property for enhanced guarantees.