FoMLAS2023: Volume InformationProceedings of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems7 articles•82 pages•Published: October 23, 2023 PapersMatthew Daggitt, Wen Kokke, Ekaterina Komendantskaya, Robert Atkey, Luca Arnaboldi, Natalia Slusarz, Marco Casadio, Ben Coke and Jeonghyeon Lee 1-5 | Mahum Naseer, Osman Hasan and Muhammad Shafique 6-28 | Avraham Raviv, Yuval Gerber, Liri Benzinou, Michelle Aluf-Medina and Hillel Kugler 29-34 | Edoardo Manino, Bernardo Magri, Mustafa Mustafa and Lucas Cordeiro 35-46 | Stefano Demarchi, Dario Guidotti, Luca Pulina and Armando Tacchella 47-58 | Marco Casadio, Luca Arnaboldi, Matthew Daggitt, Omri Isac, Tanvi Dinkar, Daniel Kienitz, Verena Rieser and Ekaterina Komendantskaya 59-70 | David Boetius and Stefan Leue 71-82 |
Keyphrasesabstract interpretation2, adversarial training2, Artificial Intelligence, bias, deep learning, Deep Neural Networks, domain-specific languages, formal analysis, formal verification, homomorphic encryption, Hyperproperties, Input Node Sensitivity, Lipschitz constant, machine learning, Model Checking., Neural Network Verification3, neural networks verification, NLP, noise tolerance, polynomial approximation, privacy-preserving machine learning, programming languages, Reinforcement Learning, robustness, Safe Machine Learning, Software Engineering, state space reduction, Trustworthy Machine Learning, types. |
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