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.

18 papers of 8,653Sort Recent · Most cited
  1. 2026
    Locally Linear Continual Learning for Time Series Based on VC-Theoretical Generalization BoundsYan V. G. Ferreira, Igor Barbosa Lima, Pedro H. G. Mapa S. … A. P. BragaTPAMI · Universidade Federal de Minas Gerais · University of Alberta
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  2. 2024
    Loss of plasticity in deep continual learningShibhansh Dohare, Juan Hernandez-Garcia, Qingfeng Lan … Richard S. SuttonNature · University of Alberta · Canadian Institute for Advanced Research
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  3. 2023
    Measuring and Mitigating Interference in Reinforcement LearningVincent Liu, Wang, Han, Tao, Ruo Yu … White, MarthaCoLLAs · University of Alberta
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  4. 2022
    What’s a good prediction? Challenges in evaluating an agent’s knowledgeAlex Kearney, Anna Koop, Patrick M. PilarskiAdaptive Behavior · University of Alberta
  5. 2022
    From eye-blinks to state construction: Diagnostic benchmarks for online representation learningBanafsheh Rafiee, Zaheer Abbas, Sina Ghiassian … Adam WhiteAdaptive Behavior · University of Alberta · University of Warwick
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  6. 2023
    KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New ClassesJianjun Zhang, Ting Wang, Wing W. Y. Ng, Witold PedryczTNNLS · South China University of Technology · Guangzhou First People's Hospital · +2
  7. 2021
    Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessShibhansh Dohare, Richard S. Sutton, A. Rupam MahmoodarXiv · University of Alberta
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  8. 2020
    Improving Performance in Reinforcement Learning by Breaking Generalization in Neural NetworksSina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam WhiteAdaptive Agents and Multi-Agents Systems · University of Alberta
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  9. 2019
    Learning Sparse Representations Incrementally in Deep Reinforcement LearningJ. Fernando Hernandez-Garcia, Richard S. SuttonarXiv · University of Alberta
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  10. 2019PDF ↗
  11. 2019PDF ↗
  12. 2019
    Is Fast Adaptation All You Need?Khurram Javed, Hengshuai Yao, Martha WhitearXiv · University of Alberta · Huawei Technologies (China)
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  13. 2019
    AMP: Adaptive Masked Proxies for Few-Shot SegmentationMennatullah Siam, Boris N. Oreshkin, Martin JägersandICCV · University of Alberta
  14. 2019
    The Utility of Sparse Representations for Control in Reinforcement LearningVincent Liu, Raksha Kumaraswamy, Lei Le, Martha WhiteAAAI · University of Alberta · Indiana University Bloomington
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  15. 2019
    Meta-Learning Representations for Continual LearningKhurram Javed, Martha WhiteNeurIPS · University of Alberta
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  16. 2019
    Adaptive Masked Weight Imprinting for Few-Shot SegmentationMennatullah Siam, Boris N. Oreshkin, Martin JägersandarXiv · University of Alberta
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  17. 2019
    An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data StreamsMahardhika Pratama, Witold Pedrycz, Geoffrey I. WebbIEEE Transactions · Nanyang Technological University · University of Alberta · +1
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  18. 2018
    Accelerating Learning in Constructive Predictive Frameworks with the Successor RepresentationCraig Sherstan, Marlos C. Machado, Patrick M. PilarskiIROS · University of Alberta
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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.