VNN-LIB 2.0 defines a network theory abstraction, formal query syntax, type system over numeric domains, and Agda-mechanized semantics to provide rigorous foundations for neural network verification independent of evolving model formats.
In: Proceedings of the 4th international workshop on Types in language design and implementation
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Effect systems are formally related to abstract interpretations via embeddings of effect quantales into abstract domains and recovery of quantales as event-based interpretations.
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VNN-LIB 2.0: Rigorous Foundations for Neural Network Verification
VNN-LIB 2.0 defines a network theory abstraction, formal query syntax, type system over numeric domains, and Agda-mechanized semantics to provide rigorous foundations for neural network verification independent of evolving model formats.
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Effect Systems as Abstract Interpretations
Effect systems are formally related to abstract interpretations via embeddings of effect quantales into abstract domains and recovery of quantales as event-based interpretations.