HyperCertificates combine closure certificates for lookahead with barrier and ranking functions to verify discrete-time systems against HyperLTL specifications.
Advances in neural information processing systems , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A reinforcement learning policy learns to adaptively harvest data samples, improving empirical constraint satisfaction and training efficiency for Lyapunov NNs and PINNs.
citing papers explorer
-
HyperCertificates: Verification of Discrete-time Dynamical Systems against HyperLTL Specifications
HyperCertificates combine closure certificates for lookahead with barrier and ranking functions to verify discrete-time systems against HyperLTL specifications.
-
Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
A reinforcement learning policy learns to adaptively harvest data samples, improving empirical constraint satisfaction and training efficiency for Lyapunov NNs and PINNs.