Search Results for author:
Found 8 papers, 4 papers with code
Neural Continuous-Time Supermartingale Certificates
We introduce for the first time a neural-certificate framework for continuous-time stochastic dynamical systems.
Neural Model Checking
Our new approach combines machine learning and symbolic reasoning by using neural networks as formal proof certificates for linear temporal logic.
Bisimulation Learning
We introduce a data-driven approach to computing finite bisimulations for state transition systems with very large, possibly infinite state space.
On the Trade-off Between Efficiency and Precision of Neural Abstraction
Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models.
Neural Abstractions
We present a novel method for the safety verification of nonlinear dynamical models that uses neural networks to represent abstractions of their dynamics.
Neural Termination Analysis
We introduce a novel approach to the automated termination analysis of computer programs: we use neural networks to represent ranking functions.
Shielding Atari Games with Bounded Prescience
We present the first exact method for analysing and ensuring the safety of DRL agents for Atari games.
Formal Synthesis of Lyapunov Neural Networks
We employ a counterexample-guided approach where a numerical learner and a symbolic verifier interact to construct provably correct Lyapunov neural networks (LNNs).
