The Complexity of Verifying Feedforward Neural Networks in Quantised Settings
We investigate the computational complexity of neural network verification in quantised settings. We distinguish three classes of Feedforward Neural Networks (FNNs): rational FNNs with exact rational weights, quantised FNNs whose weights come from a finite-width arithmetic, and dynamically quantised FNNs in which rational networks are evaluated with respect to a given finite-width arithmetic. We consider two types of specifications used in the literature. Linear programming (LP) specifications are conjunctions of linear constraints, while bit-vector (BV) specifications allow reasoning at the bit level and can express non-linear constraints. Our results give a complexity landscape of these...
| Authors | Eric Alsmann, Martin Lange, Marco Sälzer |
|---|---|
| Primary category | cs.CC |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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