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Probabilistic Deep Learning: Harnessing Bayesian Techniques for Uncertainty Estimation
Bayesian Deep Learning has emerged as a powerful framework for modelling uncertainty in deep neural networks. In traditional deep learning, models are often treated as deterministic, providing point estimates for predictions. However, in many real-world applications, it is crucial to quantify uncertainty, especially when dealing with limited data, noisy measurements, or safety-critical systems. This paper provides an overview of Bayesian Deep Learning and its applications for uncertainty estimation. We explore the foundational concepts, methodologies, and practical techniques for incorporating Bayesian principles into deep neural networks. Key topics covered include probabilistic modelling, Bayesian neural networks, variational inference, and Monte Carlo dropout. We discuss how Bayesian Deep Learning can be applied to various domains, including computer vision, natural language processing, reinforcement learning, and autonomous systems. The advantages and challenges of uncertainty estimation in these applications are highlighted. Furthermore, we review recent developments and open research directions in Bayesian Deep Learning, such as scalable Bayesian models, uncertainty-aware active learning, and model compression. These advancements are driving the integration of Bayesian principles into the mainstream of machine learning, enabling more robust and reliable decision-making in AI systems. Overall, this paper serves as a comprehensive introduction to Bayesian Deep Learning, emphasizing its significance in addressing uncertainty in modern machine learning, and it provides a roadmap for researchers and practitioners interested in harnessing the power of uncertaintyaware AI systems.
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