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  • The field of verification has advanced due to the interplay of theoretical development and empirical evaluation. Benchmarks play an important role in this by supporting the assessment of the state-of-the-art and comparison of alternative verification approaches. Recent years have witnessed significant developments in the verification of deep neural networks, but diverse benchmarks representing the range of verification problems in this domain do not yet exist. This paper describes a neural network verification benchmark generator, GDVB, that systematically varies aspects of problems in the benchmark that influence verifier performance. Through a series of studies, we illustrate how GDVB can assist in advancing the sub-field of neural network verification by more efficiently providing richer and less biased sets of verification problems.
Subject
  • Deep learning
  • Emerging technologies
  • Artificial intelligence
  • Classification algorithms
  • Mathematical and quantitative methods (economics)
  • Market research
  • Artificial neural networks
  • Computational neuroscience
  • Market segmentation
  • Mathematical psychology
  • Computational statistics
  • Philosophy of archaeology
  • Archaeological theory
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