Breaking neural reasoning architectures with metamorphic relation-based adversarial examples

• Main Authors: CHAN, Alvin, MA, Lei, JUEFEI-XU, Felix, ONG, Yew-Soon, XIE, Xiaofei, XUE, Minhui, LIU, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Task analysism; Cognition; Plugs;Perturbation methods; Memory modules; Computer architecture; Computational modeling; Adversarial examples; deep learning; differentiable neural computer (DNC); supervised learning; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7050; https://ink.library.smu.edu.sg/context/sis_research/article/8053/viewcontent/felix_tnnls21_dnc.pdf
• Institution: Singapore Management University

The ability to read, reason, and infer lies at the heart of neural reasoning architectures. After all, the ability to perform logical reasoning over language remains a coveted goal of Artificial Intelligence. To this end, models such as the Turing-complete differentiable neural computer (DNC) boast...