Incorporating neuro-inspired adaptability for continual learning in artificial intelligence
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software
Link
https://www.nature.com/articles/s42256-023-00747-w.pdf
Reference75 articles.
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2. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C. & Wermter, S. Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54–71 (2019).
3. Kudithipudi, D. et al. Biological underpinnings for lifelong learning machines. Nat. Mach. Intell. 4, 196–210 (2022).
4. McCloskey, M. & Cohen, N. J. Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motiv. 24, 109–165 (1989).
5. McClelland, J. L., McNaughton, B. L. & O’Reilly, R. C. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419 (1995).
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