In this paper we evaluate state-of-the-art SMT solvers on encodings of verification problems involving Multi-Layer Perceptrons (MLPs), a widely used type of neural network. Verification is a key technology to foster adoption of MLPs in safety-related applications, where stringent requirements about performance and robustness must be ensured and demonstrated. While safety problems for MLPs can be attacked solving Boolean combinations of linear arithmetic constraints, the gener- ated encodings are hard for current state-of-the-art SMT solvers, limiting our ability to verify MLPs in practice.
Checking Safety of Neural Networks with SMT Solvers: A Comparative Evaluation
TACCHELLA, ARMANDO
2011-01-01
Abstract
In this paper we evaluate state-of-the-art SMT solvers on encodings of verification problems involving Multi-Layer Perceptrons (MLPs), a widely used type of neural network. Verification is a key technology to foster adoption of MLPs in safety-related applications, where stringent requirements about performance and robustness must be ensured and demonstrated. While safety problems for MLPs can be attacked solving Boolean combinations of linear arithmetic constraints, the gener- ated encodings are hard for current state-of-the-art SMT solvers, limiting our ability to verify MLPs in practice.
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