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.

8 papers of 8,653Sort Recent · Most cited
  1. 2024
    The limitations of automatically generated curricula for continual learningAnna Kravchenko, Rhodri CusackPLOS · Radboud University Nijmegen · Trinity College Dublin
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  2. 2023
    Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical ComplexitySofia Leite, Bruno Mota, António Ramos Silva … Pedro Pereira RodriguesPLOS · Centre for Health Technology and Services Research · Universidade Federal do Rio de Janeiro · +5
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  3. 2023
    Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signalsTimo Flesch, Dávid Nagy, Andrew Saxe, Christopher SummerfieldPLOS · University of Oxford · HUN-REN Wigner Research Centre for Physics · +5
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  4. 2022
    The geometry of representational drift in natural and artificial neural networksKyle Aitken, Marina Garrett, Shawn R. Olsen, Ştefan MihalaşPLOS · Allen Institute
  5. 2022
    Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representationRyan Golden, Jean Erik Delanois, Pavel Šanda, Maxim BazhenovPLOS · University of California San Diego · Czech Academy of Sciences · +1
  6. 2017
    Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networksRoby Velez, Jeff ClunePLOS · University of Wyoming · Uber AI (United States)
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  7. 2015
    Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old SkillsKai Olav Ellefsen, Jean-Baptiste Mouret, Jeff ClunePLOS · Norwegian University of Science and Technology · Centre National de la Recherche Scientifique · +3
  8. 2014
    Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing EffectAndreas Knoblauch, Edgar Körner, Ursula Körner, Friedrich T. SommerPLOS · Honda (Germany) · Albstadt-Sigmaringen University · +2
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.