Papers › A Symbolic Neural Network Representation and its Application to Understanding,...
A Symbolic Neural Network Representation and its Application to Understanding, Verifying, and Patching Networks
Matthew Sotoudeh, Aditya V. Thakur
Analysis and manipulation of trained neural networks is a challenging and important problem. We propose a symbolic representation for piecewise-linear neural networks and discuss its efficient computation. With this representation, one can translate the problem of analyzing a complex neural network into that of analyzing a finite set of affine functions. We demonstrate the use of this representation for three applications. First, we apply the symbolic representation to computing weakest preconditions on network inputs, which we use to exactly visualize the advisories made by a network meant to operate an aircraft collision avoidance system. Second, we use the symbolic representation to compute strongest postconditions on the network outputs, which we use to perform bounded model checking on standard neural network controllers. Finally, we show how the symbolic representation can be combined with a new form of neural network to perform patching; i.e., correct user-specified behavior of the network.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections