5 citations · 9 across the 3 of their papers we have counts for
5 papers
Learning Temporal Logic Properties: an Overview of Two Recent Methods
Jean-Raphaël Gaglione, Rajarshi Roy, Nasim Baharisangari +3
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior. We summarize two me…
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise
Igor Khmelnitsky, Serge Haddad, Lina Ye +5
Angluin's L* algorithm learns the minimal (complete) deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approxim…
Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach
Jean-Raphaël Gaglione, Daniel Neider, Rajarshi Roy +2
We address the problem of inferring descriptions of system behavior using Linear Temporal Logic (LTL) from a finite set of positive and negative examples. Most of the existing appr…
Property-Directed Verification of Recurrent Neural Networks
Igor Khmelnitsky, Daniel Neider, Rajarshi Roy +6
This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a g…
Learning Interpretable Models in the Property Specification Language
Rajarshi Roy, Dana Fisman, Daniel Neider
We address the problem of learning human-interpretable descriptions of a complex system from a finite set of positive and negative examples of its behavior. In contrast to most of…