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  1. ; ; ; ( , ACM)
  2. ; ; ; ( , 30th International Conference Tools and Algorithms for the Construction and Analysis of Systems)
    Finkbeiner, Bernd ; Kovacs, Laura (Ed.)
    With the growing use of deep neural networks(DNN) in mis- sion and safety-critical applications, there is an increasing interest in DNN verification. Unfortunately, increasingly complex network struc- tures, non-linear behavior, and high-dimensional input spaces combine to make DNN verification computationally challenging. Despite tremen- dous advances, DNN verifiers are still challenged to scale to large ver- ification problems. In this work, we explore how the number of stable neurons under the precondition of a specification gives rise to verifica- tion complexity. We examine prior work on the problem, adapt it, and develop several novel approaches to increase stability. We demonstrate that neuron stability can be increased substantially without compromis- ing model accuracy and this yields a multi-fold improvement in DNN verifier performance. 
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    Free, publicly-accessible full text available April 6, 2025
  3. ; ; ; ( , ACM)
  4. ; ; ( , IEEE Transactions on Software Engineering)
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