University of Cambridge > Talks.cam > Cambridge Analysts' Knowledge Exchange > Learning a regularisation functional

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Learning a regularisation functional

Download to your calendar using vCal

If you have a question about this talk, please contact Ollie McEnteggart .

In the light of the immense success of artificial neural networks in machine learning computer vision, hopes have been raised that these methods applied to inverse problems could also lead to better performance. We discuss the limitations of existing algorithms for deep learning in inverse problems and then consider the approach of training a neural network as a regularisation functional. We will propose a new algorithm that relies on recent advances in generative modelling using adversarial networks. We analyse theoretical properties of this algorithm and present first computational results.

This talk is part of the Cambridge Analysts' Knowledge Exchange series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

SciBar Lecture Theatre 1, Department of Chemical Engineering & Biotechnology, New Museum Site, Pembroke Street CB2 3RA Average on weekdays: 9024.81568627 kWh

Other talks

Online Causal Inference Seminar Shining a light on the origin of supermassive black holes with cosmological simulations The physics of decision: how competitive systems compute, route, and remember TOWARD AFFORDABLE PREVENTIVE HEALTHCARE: How basic science addresses grand challenges Title to be confirmed Group Work
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity