Updates are available Correction dated 2024-10-12
Click to view Correction: https://doi.org/10.1007/s10618-024-01072-5
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Authors
Shiao, William https://orcid.org/0000-0001-5813-2266
Papalexakis, Evangelos E. https://orcid.org/0000-0002-3411-8483
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Funding
Funding for this research was provided by:
Army Research Laboratory (W911NF-13-2-0045)
Snap
National Science Foundation (IIS 2046086)
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More Information
Article History
Received: 30 December 2023
Accepted: 16 August 2024
First Online: 26 September 2024
Change Date: 12 October 2024
Change Type: Correction
Change Details: A Correction to this paper has been published:
Change Details: https://doi.org/10.1007/s10618-024-01072-5
Declarations
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: The authors declare no competing interests.
: One important ethical consideration regarding this work can arise if it is used on sensitive information like financial or healthcare data. As with all machine learning methods, our method is not infallible and may produce incorrect results. In certain mission-critical systems, that can potentially lead to severe consequences if care is not taken to validate model results. Another ethical concern is the possibility of adversarial attacks on the model. In our work, we do not consider the case of adversarially crafted input tensors designed to trick the model into performing poorly or predicting an extremely high rank, which could potentially result in maliciously altered results.
