[30] Dustin Tran - Probabilistic Programming for Deep Learning
The Thesis Review
Navigating Variational Gaussian Processes
This chapter explores the development and significance of variational Gaussian processes within Bayesian non-parametric models. It highlights the interplay between deep learning and traditional statistical methods, especially in variational inference, while addressing challenges and methodologies like generative adversarial networks. The discussion also emphasizes the interdisciplinary approach required to bridge classical methods and innovative deep learning techniques for improved model understanding and performance.
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