16 citations · 59 across the 32 of their papers we have counts for
6 papers · 1 filter
Parameterized Synthesis with Safety Properties
Oliver Markgraf, Chih-Duo Hong, Anthony W. Lin +2
Parameterized synthesis offers a solution to the problem of constructing correct and verified controllers for parameterized systems. Such systems occur naturally in practice (e.g.,…
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…
Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis
Daniel Neider, Bishwamittra Ghosh
We propose a novel approach to understanding the decision making of complex machine learning models (e.g., deep neural networks) using a combination of probably approximately corre…
Resilient Abstraction-Based Controller Design
Stanly Samuel, Kaushik Mallik, Anne-Kathrin Schmuck +1
We consider the computation of resilient controllers for perturbed non-linear dynamical systems w.r.t. linear-time temporal logic specifications. We address this problem through th…
A Formal Language Approach to Explaining RNNs
Bishwamittra Ghosh, Daniel Neider
This paper presents LEXR, a framework for explaining the decision making of recurrent neural networks (RNNs) using a formal description language called Linear Temporal Logic (LTL).…
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…