Variance stabilised optimisation of neural networks: a case study in additive manufacturing

Notley, S.V., Chen, Y., Lee, P.D. et al. (1 more author) (2021) Variance stabilised optimisation of neural networks: a case study in additive manufacturing. In: Proceedings of 2021 International Joint Conference on Neural Networks (IJCNN). 2021 International Joint Conference on Neural Networks (IJCNN), 18-22 Jul 2021, Virtual conference (Shenzhen, China). Institute of Electrical and Electronics Engineers Inc. . ISBN 9781665445979

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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: Variance stabilisation; neural network; multilayer perceptron; reduced chi-square; chi-square per degree of freedom; metal additive manufacturing
Dates:
  • Accepted: 30 April 2021
  • Published (online): 20 September 2021
  • Published: 20 September 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 07 May 2021 09:51
Last Modified: 20 Oct 2021 13:35
Status: Published
Publisher: Institute of Electrical and Electronics Engineers Inc.
Refereed: Yes
Identification Number: https://doi.org/10.1109/IJCNN52387.2021.9533311
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Filename: IJCNN2021_NN_stabilisation_paper.pdf

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