Independent Researcher
Review Article
World Journal of Advanced Research and Reviews, 2021, 11(03), 482-490
Received on 30 July 2021; revised on 09 September 2021; accepted on 11 September 2021
Uncertainty quantification (UQ) in deep neural networks (DNNs) is an essential area of research, particularly for enhancing the reliability and safety of autonomous decision-making systems deployed in high-stakes environments such as autonomous vehicles, healthcare, and robotics. This article provides a comprehensive overview of the key techniques for UQ in DNNs, including Bayesian Neural Networks, Monte Carlo Dropout, ensemble methods, and Gaussian Processes, highlighting their respective strengths and limitations. The applications of UQ in critical domains are examined, demonstrating how these techniques contribute to safer and more informed decision-making processes. The article also discusses the challenges faced in implementing UQ, such as computational complexity, scalability, and interpretability, as well as the limitations of current methods. Future directions for research are explored, emphasizing the need for more efficient, interpretable, and scalable UQ techniques, as well as the importance of integrating UQ into the AI development lifecycle and addressing ethical considerations. The article concludes by underscoring the critical role of UQ in the advancement of robust and trustworthy AI systems capable of operating effectively in uncertain real-world environments.
Uncertainty Quantification; Deep Neural Networks; Autonomous Decision-Making; Bayesian Neural Networks; Monte Carlo Dropout; Ensemble Methods; Gaussian Processes.
Preview Article PDF
Aditya Mehra. Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 2021, 11(3), 482-490. Article DOI: https://doi.org/10.30574/wjarr.2021.11.3.0421