Sep 22, 2020 · This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a ...

Oct 12, 2021 · This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a ...

Abstract. Recurrent neural networks (RNNs) are a state-of-the-art tool to represent and learn sequence-based models. They are increasingly used in ...

May 7, 2024 · Bibliographic details on Property-Directed Verification and Robustness Certification of Recurrent Neural Networks.

Jul 1, 2024 · Property-Directed Verification and Robustness Certification of Recurrent Neural Networks. Igor Khmelnitsky (1, 2) , Daniel Neider (3) ...

Oct 18, 2021 · This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite ...

An important instance of this setting is adversarial robustness certification, which measures a neural network's resilience against adversarial examples.

Nov 16, 2022 · This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a ...

Dec 28, 2020 · This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite ...

This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a ...