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Found 5 papers, 4 papers with code
Efficient compilation of expressive problem space specifications to neural network solvers
Recent work has described the presence of the embedding gap in neural network verification.
Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs
Neuro-symbolic programs -- programs containing both machine learning components and traditional symbolic code -- are becoming increasingly widespread.
Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers
However, although work has managed to incorporate the results of these verifiers to prove larger properties of individual systems, there is currently no general methodology for bridging the gap between verifiers and interactive theorem provers (ITPs).
Dijkstra Monads for All
This paper proposes a general semantic framework for verifying programs with arbitrary monadic side-effects using Dijkstra monads, which we define as monad-like structures indexed by a specification monad.
Programming Languages
Strategy Preserving Compilation for Parallel Functional Code
Performance results on GPUs and a multicore CPU show that the formalised translation process generates low-level code with performance on a par with code generated from ad hoc approaches.
Distributed, Parallel, and Cluster Computing Programming Languages
