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.
