1 citations · 1 across the 3 of their papers we have counts for
3 papers
GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning
Mark Chevallier, Filip Smola, Richard Schmoetten +1
We present GradSTL, the first fully comprehensive implementation of signal temporal logic (STL) suitable for integration with neurosymbolic learning. In particular, GradSTL can suc…
Formally Verified Neurosymbolic Trajectory Learning via Tensor-based Linear Temporal Logic on Finite Traces
Mark Chevallier, Filip Smola, Richard Schmoetten +1
We present a novel formalisation of tensor semantics for linear temporal logic on finite traces (LTLf), with formal proofs of correctness carried out in the theorem prover Isabelle…
Constrained Training of Neural Networks via Theorem Proving
Mark Chevallier, Matthew Whyte, Jacques D. Fleuriot
We introduce a theorem proving approach to the specification and generation of temporal logical constraints for training neural networks. We formalise a deep embedding of linear te…