Exploring the Intersection of Bayesian deep learning and Gaussian processes

Kozyrskiy, Bogdan

Deep learning played a significant role in establishing machine learning as a must-have instrument in multiple areas. The use of deep learning poses several challenges. Deep learning requires a lot of computational power for training and applying models. Another problem with deep learning is its inability to estimate the uncertainty of the predictions, which creates obstacles in risk-sensitive applications. This thesis presents four projects to address these problems:

- We propose an approach making use of Optical Processing Units to reduce energy consumption and speed up the inference of deep models.

- We address the problem of uncertainty estimates for classification with Bayesian inference. We introduce techniques for deep models that decreases the cost of Bayesian inference

- We developed a novel framework to accelerate Gaussian Process regression.

- We propose a technique to impose meaningful functional priors for deep models through Gaussian Processes.

Data Science
Eurecom Ref:
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