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.

5 papers of 8,653Sort Recent · Most cited
  1. 2025
    A continual imitation learning benchmark for mobile robot navigation in sequential environmentsRui Li, Y. H. Xie, Lin Zhang … Wei ZhangRobotic Intelligence and Automation · Shandong University · University of Jinan · +1
  2. 2020
    AdapterFusion: Non-Destructive Task Composition for Transfer LearningJonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé … Iryna GurevychEACL · Technische Universität Darmstadt · Supélec · +5
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  3. 2020
    Meta-Learning for Natural Language Understanding under Continual Learning FrameworkJiacheng Wang, Yong Fan, Duo Jiang, Shiqing LiarXiv · Supélec · University of Applied Sciences and Arts of Southern Switzerland · +1
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  4. 2020
    Variational Auto-Regressive Gaussian Processes for Continual LearningSanyam Kapoor, Theofanis Karaletsos, Thang D. BuiICML · Supélec · University of Applied Sciences and Arts of Southern Switzerland · +3
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  5. 2019
    Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to ForgetGuy Davidson, Michael C. MozerCVPR · Supélec · University of Applied Sciences and Arts of Southern Switzerland · +2
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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. 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.