Correlated chained Gaussian processes for datasets with multiple annotators

Gil-Gonzalez, J., Giraldo, J.-J., Alvarez-Meza, A.M. et al. (2 more authors) (2021) Correlated chained Gaussian processes for datasets with multiple annotators. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X

Abstract

Metadata

Authors/Creators:
  • Gil-Gonzalez, J.
  • Giraldo, J.-J.
  • Alvarez-Meza, A.M.
  • Orozco-Gutierrez, A.
  • Alvarez, M.A.
Copyright, Publisher and Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Multiple annotators; Correlated Chained Gaussian Processes; Variational inference; Semi-parametric latent factor model
Dates:
  • Accepted: 26 September 2021
  • Published (online): 11 October 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
FunderGrant number
Engineering and Physical Sciences Research CouncilEP/R034303/1; EP/T00343X/1
Rosetrees Trustn/a
Depositing User: Symplectic Sheffield
Date Deposited: 22 Oct 2021 06:14
Last Modified: 11 Oct 2022 00:13
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: https://doi.org/10.1109/tnnls.2021.3116943

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