A Stanford Intelligent Systems Laboratory (SISL) research group has announced it is open-sourcing its NeuralVerification.jl project, which helps verify deep neural networks’ training, robustness and safety results.

Project Resource: https://github.com/sisl/NeuralVerification.jl?from=timeline#set-up-the-problem

The library is now available in GitHub and contains implementations of various methods used to verify deep neural networks. The resource divides methods to verify whether a neural network satisfies certain input-output constraints into five categories, including:

Reachability methods: ExactReach, MaxSens, Ai2,

Primal optimization methods: NSVerify, MIPVerify, ILP

Dual optimization methods: Duality, ConvDual, Certify

Search and reachability methods: ReluVal, DLV, FastLin, FastLip

Search and optimization methods: Sherlock, BaB, Planet, Reluplex

The library’s installation instructions are as follows:

Publication of the Stanford team’s related research paper is expected by the end of January 2019 at the earliest.