Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

14 papers of 8,653Sort Recent · Most cited
  1. 2026
    Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors From a Bi-level Optimization PerspectiveHong Wang, Renzhen Wang, Yichen Wu … Deyu MengTPAMI · Xi'an Jiaotong University · Harvard University · +1
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  2. 2026
    Order parameters and phase transitions of continual learning in deep neural networksHaozhe Shan, Qianyi Li, Haim SompolinskyPNAS · Harvard University · Harvard University Press · +4
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  3. 2025
    Overcoming classic challenges for artificial neural networks by providing incentives and practiceKazuki Irie, Brenden M. LakeNature Machine Intelligence · Harvard University · Princeton University
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  4. 2025
    An invariant schema emerges within a neural network during hierarchical learning of visual boundariesJames T. Elder, Jie Zheng, Lydia B. Shimelis … Milo M. LinbioRxiv · Center for Systems Biology · The University of Texas Southwestern Medical Center · +5
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  5. 2024
    Reconciling shared versus context-specific information in a neural network model of latent causesQihong Lu, Tan T Nguyen, Qiong Zhang … Kenneth A. NormanScientific Reports · Princeton University · Washington University in St. Louis · +3
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  6. 2024
    Blocked training facilitates learning of multiple schemasAndre Beukers, Silvy Collin, Ross P. Kempner … Kenneth A. NormanCommunications Psychology · Princeton University · Tilburg University · +1
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  7. 2022
    Learning to Predict Gradients for Semi-Supervised Continual LearningYan Luo, Yongkang Wong, Mohan Kankanhalli, Qi ZhaoTNNLS · University of Minnesota · Harvard University · +2
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  8. 2021
    Tuned Compositional Feature Replays for Efficient Stream LearningMorgan B. Talbot, Rushikesh Zawar, Rohil Badkundri … Gabriel KreimanTNNLS · Boston Children's Hospital · Harvard–MIT Division of Health Sciences and Technology · +6
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  9. 2023
    Learning to Learn: How to Continuously Teach Humans and MachinesParantak Singh, You Li, Ankur Sikarwar … Mengmi ZhangICCV · Agency for Science, Technology and Research · Nanyang Technological University · +5
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  10. 2023
    Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical ComplexitySofia Leite, Bruno Mota, António Ramos Silva … Pedro Pereira RodriguesPLOS · Centre for Health Technology and Services Research · Universidade Federal do Rio de Janeiro · +5
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  11. 2022
    Representational drift: Emerging theories for continual learning and experimental future directions.Laura Driscoll, Lea Duncker, Christopher D. HarveyCurrent Opinion in Neurobiology · Stanford University · Howard Hughes Medical Institute · +1
  12. 2022
    Stochastic consolidation of lifelong memoryNimrod Shaham, Jay Chandra, Gabriel Kreiman, Haim SompolinskyScientific Reports · Harvard University · Hebrew University of Jerusalem · +1
  13. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
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  14. 2017
    Capacity of Neural Networks for Lifelong Learning of Composable TasksLeslie G. ValiantIEEE 58th Annual Symposium on Foundations of Computer Sci… · Harvard University
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.