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.

8 papers of 11,817Sort Recent · Most cited
  1. 2021
    Beyond gradients: Factorized, geometric control of interference and generalizationDavid M. Scott, Michael J. FrankbioRxiv · Brown University · Allen Institute for Brain Science
  2. 2021
    A biologically inspired architecture with switching units can learn to generalize across backgroundsDoris Voina, Eric Shea‐Brown, Ştefan MihalaşbioRxiv · University of Washington · University of Washington Applied Physics Laboratory · +2
  3. 2021
    Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual LearningKaran Grewal, Jérémy Forest, Benjamin P. Cohen, Subutai AhmadbioRxiv · Numerica Corporation (United States) · Lumentum (United States)
  4. 2021
    A Biologically Plausible Model for Continual Learning using Synaptic Weight AttractorsRomik Ghosh, D. Mastrovito, Ştefan MihalaşbioRxiv · Ohio University · Allen Institute · +3
  5. 2021
    Artificial Neural Networks for classification of single cell gene expressionJiahui Zhong, Minjie Lyu, Huan Jin … Vladimir BrusićbioRxiv · University of Nottingham Ningbo China · Tongji University · +5
  6. 2021
    Using top-down modulation to optimally balance shared versus separated task representationsPieter Verbeke, Tom VergutsbioRxiv · Ghent University Hospital
  7. 2021
    Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemmaSimon Schug, Frederik Benzing, Angelika StegerbioRxiv · SIB Swiss Institute of Bioinformatics · University of Zurich · +1
  8. 2021
    Meta-learning local synaptic plasticity for continual familiarity detectionDanil Tyulmankov, Guangyu Robert Yang, L. F. AbbottbioRxiv · Columbia 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. 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.