In this talk I will first present an overview of the research challenges we address in my group, in the context of probabilistic machine learning. With this context in mind, I will delve into the details of recent approaches to achieve a practical and scalable way to train deep probabilistic models built on the concept of Gaussian Processes. To this end, I will first briefly introduce what Gaussian Process inference is, then I will move on to describe a model approximation technique, based on random Fourier features, and approximate variational inference to train the resulting model. The talk will conclude with a distributed systems flavour, covering methods and systems to train large scale machine learning models, including data and model parallelism approaches.
Practical and scalable probabilistic machine learning
SMARTDATA 2018, Keynote Speech at 3rd Workshop for the Interdepartmental Center SmartData@PoliTO, September 24-25, 2018, Turin, Italy
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