In this talk we discuss the idea of data- driven regularisers for inverse imaging
problems. We are in particular interested in the combination of model-based and purely
data-driven image processing approaches. In this context we will make a journey from
“shallow” learning for computing optimal parameters for variational regularisation models
by bilevel optimization to the investigation of different approaches that use deep neural
networks for solving inverse imaging problems. This talk is based on a 2019 Acta
Numerica paper written together with Simon Arridge, Peter Maass and Ozan Öktem.
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Recent years have witnessed an increased cross-fertilisation between the fields of statistics and computer science. In the era of Big Data, statisticians are increasingly facing the question of guaranteeing prescribed levels of inferential accuracy within certain time budget. On the other hand, computer scientists are progressively modelling data as noisy measurements coming from an underlying population, exploiting the statistical regularities of the data to save on computation.
This cross-fertilisation has led to the development and understanding of many of the algorithmic paradigms that underpin modern machine learning, including gradient descent methods and generalisation guarantees, implicit regularisation strategies, high-dimensional statistical models and algorithms.
About the event
This event will bring together experts to talk about advances at the intersection of statistics and computer science in machine learning. This two-day conference will focus on the underlying theory and the links with applications, and will feature 12 talks by leading international researchers.
The intended audience is faculty, postdoctoral researchers and Ph.D. students from the UK/EU, in order to introduce them to this area of research and to the Turing.
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