Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,984Sort Recent · Most cited
  1. 2020
    Generative Continual Concept LearningMohammad Rostami, Soheil Kolouri, Praveen K. Pilly, James L. McClellandAAAI · California University of Pennsylvania · HRL Laboratories (United States) · +1
    PDF ↗
  2. 2020
    Model primitives for hierarchical lifelong reinforcement learningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferAutonomous Agents and Multi-Agent Systems · Columbia University · Stanford University
  3. 2020
    Continual Adaptation for Efficient Machine CommunicationRobert D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. GoodmanCoNLL · Princeton University · Department of Physics, Mathematics and Informatics · +1
    PDF ↗
  4. 2020
    Continuous Meta-Learning without TasksJ. Michael Harrison, Apoorva Sharma, Chelsea Finn, Marco PavoneNeurIPS · Stanford University · University of California, Berkeley
    PDF ↗
  5. 2020
    Generative Memory for Lifelong LearningXin Su, Shangqi Guo, Tian Tan, Feng ChenTNNLS · Beijing Advanced Sciences and Innovation Center · Tsinghua University · +1
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.