Neural Network Verification using Partial Multi-Neuron Relaxation
The increasing integration of deep neural networks in critical systems has spawned a theoretical and practical interest in formally guaranteeing safety properties about their behavior. To achieve this, contemporary verification algorithms rely on computing linear relaxations for a network's non-linear activation functions. Existing approaches for linear relaxations typically fall into one of two categories: single-neuron relaxation, in which each activation neuron is bounded in terms of its sources; and multi-neuron relaxation, in which linear bounds involving multiple activation neurons and their sources are calculated. However, existing methods might fail to balance tightness and scalab...
| Authors | Ido Shmuel, Guy Katz |
|---|---|
| Primary category | cs.LO |
| Published | 28 May 2026 |
| Last updated at source | 2 June 2026 |
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