PiShield: A PyTorch Package for Learning with Requirements

Autor:	Stoian, Mihaela Cătălina, Tatomir, Alex, Lukasiewicz, Thomas, Giunchiglia, Eleonora
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Logic in Computer Science
Druh dokumentu:	Working Paper
Popis:	Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology. PiShield guarantees compliance with these requirements, regardless of input. Additionally, it allows for integrating requirements both at inference and/or training time, depending on the practitioners' needs. Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains. Here, we explore three application scenarios: functional genomics, autonomous driving, and tabular data generation. Comment: Demo paper, accepted at IJCAI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.18285 Zobrazit plný text záznamu View this record from Arxiv