HMSN : Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes

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Hyperspherically Regularized Networks for Self-Supervision

This work used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk). Access granted through the project: ec173 - Next gen self-supervised learning systems for vision tasks.Preprin

S-JEA : Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning

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How might technology rise to the challenge of data sharing in agri-food?

Acknowledgement This work was supported by an award made by the UKRI/EPSRC funded Internet of Food Things Network+ grant EP/R045127/1. We would also like to thank Mr Steve Brewer and Professor Simon Pearson for supporting the work presented in this paper.Peer reviewedPostprin

Development of machine learning techniques and evaluation of analysis results

CORTEX - Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 754316.Publisher PD

Data Sharing and Interoperability for Data Trusts Workshop : Summary Report

The workshop was supported by an award made by the UKRI, EPSRC funded Internet of Food Things Network+ grant EP/R045127/1. We would like to thank Paul Mayfield, Hannah Rudman, and Steve Brewer for their contributions to the workshop as well as all our participants for your insightful discussionsPublisher PD

Detection and Localisation of Multiple In-core Perturbations with Neutron Noise-based Self-Supervised Domain Adaptation

The research conducted was made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No 754316 (CORTEX project).Peer reviewedPreprin

Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurements

The research conducted has been made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No 754316 for the “CORe Monitoring Techniques And EXperimental Validation And Demonstration (CORTEX)” Horizon 2020 project, 2017-2021.Peer reviewedPublisher PD

Results of the application and demonstration calculations

CORTEX - Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 754316.Publisher PD

Machine learning for analysis of real nuclear plant data in the frequency domain

The research conducted has been made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No 754316 for the “CORe Monitoring Techniques And EXperimental Validation And Demonstration (CORTEX)” Horizon 2020 project, 2017-2021.Peer reviewedPublisher PD