Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Yuhas, Michael"'
Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model's training distribution to prevent potentially unsafe actions. However, OOD detectors are often
Externí odkaz:
http://arxiv.org/abs/2409.00880
Autor:
Yuhas, Michael, Easwaran, Arvind
Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have
Externí odkaz:
http://arxiv.org/abs/2307.13419
Autor:
Yuhas, Michael, Easwaran, Arvind
In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle's operation. However, ML models are unlikely to make accurate decisions when
Externí odkaz:
http://arxiv.org/abs/2211.11520
Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning), are necessary to ensure the safety of results generated by machine l
Externí odkaz:
http://arxiv.org/abs/2210.09959
When machine learning (ML) models are supplied with data outside their training distribution, they are more likely to make inaccurate predictions; in a cyber-physical system (CPS), this could lead to catastrophic system failure. To mitigate this risk
Externí odkaz:
http://arxiv.org/abs/2207.14694
Publikováno v:
Yuhas, M., Feng, Y., Ng, D. J. X., Rahiminasab, Z., & Easwaran, A. (2021, May). Embedded out-of-distribution detection on an autonomous robot platform. In Proceedings of the Workshop on Design Automation for CPS and IoT (pp. 13-18)
Machine learning (ML) is actively finding its way into modern cyber-physical systems (CPS), many of which are safety-critical real-time systems. It is well known that ML outputs are not reliable when testing data are novel with regards to model train
Externí odkaz:
http://arxiv.org/abs/2106.15965
Autor:
Fellows, James A.1, Yuhas, Michael A.2
Publikováno v:
CPA Journal. Jul2007, Vol. 77 Issue 7, p42-45. 4p.
Publikováno v:
Journal of Passthrough Entities. Sep2008, Vol. 11 Issue 5, p27-52. 14p. 1 Chart.
Autor:
HARRIS, RICHARD, YUHAS, MICHAEL A.
Publikováno v:
Journal of Taxation. Jun2017, Vol. 126 Issue 6, p259-278. 20p.