Eric Alsmann
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2026
The Complexity of Verifying Feedforward Neural Networks in Quantised Settings
This paper analyzes the computational complexity of verifying feedforward neural networks when their weights are restricted to finite-width arithmetic, finding that verification remains NP-complete for fixed-precision networks and establishing upper bounds for dynamically quantized networks.
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