Uncertainty in Machine Learning and Future Computers
Unfortunately, computers are subject to uncertainty. If we can quantitatively evaluate the uncertainty in the output of ordinary computers, we can realize efficient and trustworthy computers that actively utilize fluctuating computation principles.
Authors
- Shinya Takamaeda-Yamazaki (ORCID: https://orcid.org/0000-0003-3441-1695)
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- Computer
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1109/mc.2025.3536056
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00