TO EVALUATE THE PERFORMANCE AND SCALABILITY OF BAYESIAN TECHNIQUES IN DEEP LEARNING
Keywords:
Bayesian Deep Learning, Uncertainty Estimation, Reliability, Scalability, Medical Diagnostics, Autonomous Driving, Robotics.Abstract
Since deep learning methods are being applied to high-risk applications like medical diagnosis, autonomous vehicles, and robotics, the
requirement for highly precise, robust models that can also produce dependable uncertainty estimations is imperative. This work aims at comparing the efficiency and CO tolerance of various methods of Bayesian uncertainty estimation and highlighting their ability to
improve model credibility and stability
References
M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya, V. Makarenkov, S. Nahavandi.
A review of uncertainty quantification in deep learning: techniques, applications and challenges. Information Fusion, 76 (2021), pp. 243-297.
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Published
2025-01-02
How to Cite
Ramu V ,Dr. M. Vinoth Kumar. (2025). TO EVALUATE THE PERFORMANCE AND SCALABILITY OF BAYESIAN TECHNIQUES IN DEEP LEARNING. Journal of Computational Analysis and Applications (JoCAAA), 34(1), 16–25. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1666
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