Abstract Neural Networks
| العنوان: | Abstract Neural Networks |
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| المؤلفون: | Matthew Sotoudeh, Aditya V. Thakur |
| المصدر: | Static Analysis ISBN: 9783030654733 SAS |
| بيانات النشر: | Springer International Publishing, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Artificial neural network, business.industry, Computer science, Computer Science::Neural and Evolutionary Computation, Hyperbolic function, Activation function, Parameterized complexity, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), 02 engineering and technology, Interval (mathematics), Sigmoid function, 010501 environmental sciences, 01 natural sciences, Domain (software engineering), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, 0105 earth and related environmental sciences, Abstraction (linguistics) |
| الوصف: | Deep Neural Networks (DNNs) are rapidly being applied to safety-critical domains such as drone and airplane control, motivating techniques for verifying the safety of their behavior. Unfortunately, DNN verification is NP-hard, with current algorithms slowing exponentially with the number of nodes in the DNN. This paper introduces the notion of Abstract Neural Networks (ANNs), which can be used to soundly overapproximate DNNs while using fewer nodes. An ANN is like a DNN except weight matrices are replaced by values in a given abstract domain. We present a framework parameterized by the abstract domain and activation functions used in the DNN that can be used to construct a corresponding ANN. We present necessary and sufficient conditions on the DNN activation functions for the constructed ANN to soundly over-approximate the given DNN. Prior work on DNN abstraction was restricted to the interval domain and ReLU activation function. Our framework can be instantiated with other abstract domains such as octagons and polyhedra, as well as other activation functions such as Leaky ReLU, Sigmoid, and Hyperbolic Tangent. |
| ردمك: | 978-3-030-65473-3 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::76e220af6f84edefb85bbff18514ad51 https://doi.org/10.1007/978-3-030-65474-0_4 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........76e220af6f84edefb85bbff18514ad51 |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783030654733 |
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