Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 11,817Sort Recent · Most cited
  1. 2019
    A Story of Two Streams: Reinforcement Learning Models from Human Behavior and NeuropsychiatryBaihan Lin, Guillermo Cecchi, Djallel Bouneffouf … Irina RishAdaptive Agents and Multi-Agents Systems · Columbia University · IBM (United States) · +1
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  2. 2019
    Model primitives for hierarchical lifelong reinforcement learningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferAutonomous Agents and Multi-Agent Systems · Columbia University · Stanford University
  3. 2019
    Continual Learning in a Multi-Layer Network of an Electric FishSalomon Z. Muller, Abigail N Zadina, L. F. Abbott, Nathaniel B. SawtellCell · Columbia University
  4. 2019
    Modulating the Use of Multiple Memory Systems in Value-based Decisions with Contextual NoveltyKatherine Duncan, Annika Semmler, Daphna ShohamyJournal of Cognitive Neuroscience · University of Toronto · Vrije Universiteit Amsterdam · +1
  5. 2019
    Task representations in neural networks trained to perform many cognitive tasksGuangyu Robert Yang, Madhura R. Joglekar, Hui Song … Xiao‐Jing WangNature Neuroscience · New York University · Columbia University · +5
  6. 2019
    A Progressive Model to Enable Continual Learning for Semantic Slot FillingYilin Shen, Xiangyu Zeng, Hongxia JinEMNLP · Samsung (South Korea) · Samsung (United States) · +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 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.