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
Abstract
PyKale is a Python library for Knowledge-aware machine learning from multiple sources of data to enable/accelerate interdisciplinary research. It embodies green machine learning principles to reduce repetitions/redundancy, reuse existing resources, and recycle learning models across areas. We propose a pipeline-based application programming interface (API) so all machine learning workflows follow a standardized six-step pipeline. PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, particularly multimodal learning and transfer learning. To be more accessible, it separates code and configurations to enable non-programmers to configure systems without coding. PyKale is officially part of the PyTorch ecosystem and includes interdisciplinary examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging: https://pykale.github.io/.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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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: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant 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: | 10.1145/3511808.3557676 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192608 |