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Continual learning of context-dependent processing in neural networks
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“Among popular methods in this group are Orthogonal Gradient Descent (OGD) [12], Orthogonal Weight Modification (OWM) [10], and Parameter Generation and Model Adaptation (PGMA) [11].”
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“Orthogonal projection methods have been widely studied in CNN and MLP (e.g., OWM [18], GPM [8], TRGP [19]) as a principled way to reduce task interference.”
OWM-LoRA: Projecting LoRA Gradients onto Orthogonal Subspaces
Liu, Y., Guo, L., Dai, Y., Pan, P., Zhao, C., Li, Z., & Yu, S. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729212
“By confining parameter update to this orthogonal subspace [8], the model can acquire new knowledge without interfering with representations learned on earlier tasks, thereby mitigating catastrophic forgetting while maintaining the efficiency and flexibility of LoRA.”
Influential
Metadata
- Authors
- Zeng, G., Chen, Y., Cui, B., & Yu, S.
- Affiliations
- Chinese Academy of Sciences (CN)
- ORCID
- Yu, Shan
- Venue
- Nature Machine Intelligence
- Published
- 2019-08-09
- Volume
- 1
- Issue
- 8
- Pages
- 364-372
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2895699061
- Cited by
- 309
- References
- 41
- Citations by year
- 2026: 51 · 2025: 63 · 2024: 54 · 2023: 52 · 2022: 23 · 2021: 39 · 2020: 21 · 2019: 6
- FWCI
- 10.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- continual learning, catastrophic forgetting, context-dependent processing, neural network modules, multi-task learning
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