Deep Generative Models Insights and Applications

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Author(s): N. Gayathri (GITAM University (Deemed), India), S. Rakesh Kumar (GITAM University (Deemed), India), U. Janardhan Reddy (Jain University (Deemed), India), Midde Ranjit Reddy (Srinivasa Ramanujan Institute of Technology, India)and G. Ravikanth (Koneru Lakshmaiah Education Foundation, India)
Copyright: 2025
Pages: 20
Source title: Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers
Source Author(s)/Editor(s): Abhishek Kumar (Chandigarh University, India), S. Rakesh Kumar (GITAM University (Deemed), India), N. Gayathri (GITAM University (Deemed), India), R. Srivel (Adhiparasakthi Engineering College, India)and Dhaya C. (Adhiparasakthi Engineering College, India)
DOI: 10.4018/979-8-3693-6442-0.ch015

Keywords: Biometrics / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

A fundamental framework for reasoning with probabilities in probabilistic programming languages and visual representations is generative modeling. It is among the fascinating and quickly developing areas of artificial intelligence and statistical machine learning. The latest advances in stochastic optimization techniques along with the parameterization of generative models via deep neural networks have rendered it possible to represent complicated, high-dimensional data such as speech, text, and images in a scalable manner. This chapter examines the learning algorithms and probabilistic underpinnings of deep generative models, as well as the fields of application that have recently profited from deep generative models.

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