alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, and 2024)
Lyapunov-stable Neural Control for State and Output Feedback
DIG is a numerical invariant generation tool. It infers program invariants or properties over (i) program execution traces or (ii) program source code. DIG supports many forms of numerical invariants, including nonlinear equalities, octagonal and interval properties, min/max-plus relations, and congruence relations.
Uses the simplex to propose a tighter boundary for the l1 perturbation of the convex activation function network, improving the effect of the CROWN algorithm.
Verification of Neural Network Control Systems in Continuous Time
A reorganized collection of benchmarks from VNNCOMP since 2022, divided into three categories: fully connected, convolutional, and residual networks. Each category is available as a submodule, allowing you to download individual categories or all of them at once.
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