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. 2022
    On-device synaptic memory consolidation using Fowler-Nordheim quantum-tunnelingMustafizur Rahman, Subhankar Bose, Shantanu ChakrabarttyFrontiers · Washington University in St. Louis
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  2. 2022
    Bayesian continual learning via spiking neural networksNicolas Skatchkovsky, Hyeryung Jang, Osvaldo SimeoneFrontiers · King's College London · Dongguk University
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  3. 2022
    Brain-inspired Predictive Coding Improves the Performance of Machine Challenging TasksJangho Lee, Jeonghee Jo, Byoung-Hwa Lee … Sungroh YoonFrontiers · Seoul National University · Electronics and Telecommunications Research Institute · +1
  4. 2022
    Self-organized Learning from Synthetic and Real-World Data for a Humanoid Exercise RobotNicolas Duczek, Matthias Kerzel, Philipp Allgeuer, Stefan WermterFrontiers · Universität Hamburg
  5. 2022
    A neuro-inspired computational model of life-long learning and catastrophic interference, mimicking hippocampus novelty-based dopamine modulation and lateral inhibitory plasticityPierangelo Afferni, Federico Cascino-Milani, Andrea Mattera, Gianluca BaldassarreFrontiers · Università Campus Bio-Medico · University of Würzburg · +2
  6. 2022
    Continual Sequence Modeling With Predictive CodingLouis Annabi, Alexandre Pitti, Mathias QuoyFrontiers · Centre National de la Recherche Scientifique · Equipes Traitement de l'Information et Systèmes · +2
  7. 2022
    What Is Adult Hippocampal Neurogenesis Good for?Gerd KempermannFrontiers · German Center for Neurodegenerative Diseases · Center for Regenerative Therapies Dresden · +1
  8. 2022
    Catastrophic Forgetting in Deep Graph Networks: A Graph Classification BenchmarkAntonio Carta, Andrea Cossu, Federico Errica, Davide BacciuFrontiers · University of Pisa · Scuola Normale Superiore
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.