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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.

12 papers of 11,817Sort Recent · Most cited
1995 Clear all
  1. 1995
    A functional analytic approach to incremental learning in optimally generalizing neural networksSethu Vijayakumar, Haruo OgawaICNN'95 - International Conference on Neural Networks · Tokyo Institute of Technology
  2. 1995
    Explanation-based neural network learning a lifelong learning approachSebastian ThrunKluwer international series in engineering and computer s… · Carnegie Mellon University
  3. 1995
    Is Learning The n-th Thing Any Easier Than Learning The First?S. ThrunNeurIPS
  4. 1995
    A neural network architecture for incremental learningShigetoshi Shiotani, Toshio Fukuda, Takanori ShibataNeurocomputing · Nagoya University
  5. 1995
    An Analysis of Catastrophic InterferenceNoel Sharkey, Amanda SharkeyConnection Science
  6. 1995
    NMDA antagonism during development extends sparing of hindlimb function to older spinally transected rats.Donna L. Maier, Robert G. Kalb, Dennis J. StelznerDevelopmental Brain Research · Syracuse University · Yale University
  7. 1995
    Catastrophic Forgetting, Rehearsal and PseudorehearsalAnthony RobinsConnection Science · University of Otago
  8. 1995
    Incremental Learning with Optimal Generalizing Ability in Neural NetworksS. Vijayakumar, H. OgawaPreprint
  9. 1995
    Fast-learning VIEWNET architectures for recognizing three-dimensional objects from multiple two-dimensional viewsGary Bradski, Stephen GrossbergNeural Networks · Boston University
  10. 1995
    Catastrophic interference in neural networksStephan Lewandowsky, Shu LiElsevier
  11. 1995
    Continual learning in reinforcement environmentsMark B. RingGMD-Bericht
  12. 1995
    Catastrophic Forgetting; Catastrophic Interference; Stability; Plasticity; Rehearsal.Authors pendingConnection Science
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.