Data-driven methods, in particular those based on Deep Learning, have become some of the most important and widely used approaches for inverse problems. Of particular note are learned regularisation approaches, which allow a synergy between data-driven approaches and theoretical framework from classical theory. This is a growing research area that includes unrolled optimisation, learning-to-optimise, regularisation by denoising, adverserial regularisation, bilevel learning, and other learning frameworks.

This workshop, supported by EPSRC programme grant Maths4DL, aims to bring together researchers working at the cutting edge of learned regularisation and their applications.

More information can be found at https://learnedregulariser.github.io/

Registration for this event is now closed.