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@inproceedings{2392233, author = {Chau, Calvin and Křetínský, Jan and Mohr, Stefanie}, address = {Singapore}, booktitle = {Automated Technology for Verification and Analysis. ATVA 2023}, doi = {http://dx.doi.org/10.1007/978-3-031-45329-8_19}, keywords = {Abstraction; Machine learning; Neural network}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Singapore}, isbn = {978-3-031-45328-1}, pages = {401-421}, publisher = {Springer}, title = {Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks}, year = {2023} }
TY - JOUR ID - 2392233 AU - Chau, Calvin - Křetínský, Jan - Mohr, Stefanie PY - 2023 TI - Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks PB - Springer CY - Singapore SN - 9783031453281 KW - Abstraction KW - Machine learning KW - Neural network N2 - Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework, where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision. ER -
CHAU, Calvin, Jan KŘETÍNSKÝ a Stefanie MOHR. Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks. Online. In \textit{Automated Technology for Verification and Analysis. ATVA 2023}. Singapore: Springer, 2023, s.~401-421. ISBN~978-3-031-45328-1. Dostupné z: https://dx.doi.org/10.1007/978-3-031-45329-8\_{}19.
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