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.

11 papers of 8,653Sort Recent · Most cited
  1. 2022
    Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic EnvironmentsAbhiram Iyer, Karan Grewal, Akash Velu … Subutai AhmadFrontiers · Carnegie Mellon University · Stanford University · +1
    PDF ↗
  2. 2021
    Replay in Deep Learning: Current Approaches and Missing Biological ElementsTyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov … Christopher KananNeural Computation · Rochester Institute of Technology · University of California San Diego · +5
    PDF ↗
  3. 2021
    Incremental Learning via Rate ReductionZiyang Wu, Christina Baek, Chong You, Yi MaCVPR · Cornell University · Berkeley College · +1
    PDF ↗
  4. 2021
    Selective Replay Enhances Learning in Online Continual Analogical ReasoningTyler L. Hayes, Christopher KananCVPR · Rochester Institute of Technology · Cornell University · +1
    PDF ↗
  5. 2021
    Max-Margin Deep Diverse Latent Dirichlet Allocation With Continual LearningWenchao Chen, Bo Chen, Yingqi Liu … Long TianIEEE Trans. Cybernetics · Xidian University · Cornell University · +1
  6. 2021
    Continual Learning for Grounded Instruction Generation by Observing Human Following BehaviorNoriyuki Kojima, Alane Suhr, Yoav ArtziTACL · Cornell University · Cornell Tech
    PDF ↗
  7. 2021
    Anatomy of Catastrophic Forgetting: Hidden Representations and Task SemanticsVinay Ramasesh, Ethan Dyer, Maithra RaghuICLR · Google (United States) · Cornell University
    PDF ↗
  8. 2021
    Graph-Based Continual LearningBinh Tang, David S. MattesonICLR · Cornell University
    PDF ↗
  9. 2020
    Rapid online learning and robust recall in a neuromorphic olfactory circuitNabil Imam, Thomas A. ClelandNature Machine Intelligence · Intel (United States) · Cornell University
    PDF ↗
  10. 2020
    REMIND Your Neural Network to Prevent Catastrophic ForgettingTyler L. Hayes, Kushal Kafle, Robik Shrestha … Christopher KananECCV · Rochester Institute of Technology · Adobe Systems (United States) · +2
    PDF ↗
  11. 2019
    A Spike Time-Dependent Online Learning Algorithm Derived From Biological OlfactionAyon Borthakur, Thomas A. ClelandFrontiers · Cornell 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.