PyKale: Knowledge-aware machine learning from multiple sources in Python

Lu, H. orcid.org/0000-0002-0349-2181, Liu, X., Zhou, S. et al. (6 more authors) (2022) PyKale: Knowledge-aware machine learning from multiple sources in Python. In: Hasan, M.A. and Xiong, L., (eds.) CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, 17-21 Oct 2022, Atlanta GA USA. ACM , pp. 4274-4278. ISBN 9781450392365

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is an author-produced version of a paper subsequently published in CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: machine learning; multimodal learning; transfer learning; PyTorch
Dates:
  • Published (online): 17 October 2022
  • Published: 17 October 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
FunderGrant number
WELLCOME TRUST (THE)215799/Z/19/Z
Depositing User: Symplectic Sheffield
Date Deposited: 25 Oct 2022 15:36
Last Modified: 25 Oct 2022 15:36
Status: Published
Publisher: ACM
Refereed: Yes
Identification Number: https://doi.org/10.1145/3511808.3557676

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