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Towards Causal Replay for Knowledge Rehearsal in Continual Learning
Date
2023-01-01
Author
Churamani, Nikhil
Cheong, Jiaee
Kalkan, Sinan
Gunes, Hatice
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Given the challenges associated with the real-world deployment of Machine Learning (ML) models, especially towards efficiently integrating novel information on-the-go, both Continual Learning (CL) and Causality have been proposed and investigated individually as potent solutions. Despite their complementary nature, the bridge between them is still largely unexplored. In this work, we focus on causality to improve the learning and knowledge preservation capabilities of CL models. In particular, positing Causal Replay for knowledge rehearsal, we discuss how CL-based models can benefit from causal interventions towards improving their ability to replay past knowledge in order to mitigate forgetting.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85170394737&origin=inward
https://hdl.handle.net/11511/105697
Conference Name
1st AAAI Bridge Program on Continual Causality
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Department of Computer Engineering, Conference / Seminar
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BibTeX
N. Churamani, J. Cheong, S. Kalkan, and H. Gunes, “Towards Causal Replay for Knowledge Rehearsal in Continual Learning,” Washington, Amerika Birleşik Devletleri, 2023, vol. 208, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85170394737&origin=inward.