4 papers
Robustness Verification of Graph Neural Networks Via Lightweight Satisfiability Testing
Chia-Hsuan Lu, Tony Tan, Michael Benedikt
Graph neural networks (GNNs) are the predominant architecture for learning over graphs. As with any machine learning model, an important issue is the detection of attacks, where an…
Extracting Robust Register Automata from Neural Networks over Data Sequences
Chih-Duo Hong, Hongjian Jiang, Anthony W. Lin +3
Automata extraction is a method for synthesising interpretable surrogates for black-box neural models that can be analysed symbolically. Existing techniques assume a finite input a…
Analysis of logics with arithmetic
Michael Benedikt, Chia-Hsuan Lu, Tony Tan
We present new results on finite satisfiability of logics with counting and arithmetic. One result is a tight bound on the complexity of satisfiability of logics with so-called loc…
Decidability of Graph Neural Networks via Logical Characterizations
Michael Benedikt, Chia-Hsuan Lu, Tony Tan
We present results concerning the expressiveness and decidability of a popular graph learning formalism, graph neural networks (GNNs), exploiting connections with logic. We use a f…