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.

7 papers of 11,817Sort Recent · Most cited
  1. 2021
    Runtime Analyses of the Population-Based Univariate Estimation of Distribution Algorithms on LeadingOnesPer Kristian Lehre, Phan Trung Hai NguyenAlgorithmica · University of Birmingham
  2. 2021
    Continual Learning of Knowledge Graph EmbeddingsAngel Daruna, Mehul Gupta, Mohan Sridharan, Sonia ChernovaRA-L · Georgia Institute of Technology · University of Birmingham
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  3. 2020
    GloDyNE: Global Topology Preserving Dynamic Network EmbeddingChengbin Hou, Han Zhang, Shan He, Ke TangTKDE · Southern University of Science and Technology · University of Birmingham
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  4. 2016
    Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web MiningYoung Jay, Valerio Basile, Kunze Lars … Nick HawesFrontiers · University of Birmingham · Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis
  5. 2009
    Evolved Dual Weight Neural Architectures to Facilitate Incremental LearningJohn A. BullinariaInternational Joint Conference on Computational Intelligence · University of Birmingham
  6. 2005
    Evolving improved incremental learning schemes for neural network systemsTebogo Seipone, John A. BullinariaIEEE Congress on Evolutionary Computation · University of Birmingham
  7. 2005
    EVOLVING NEURAL NETWORKS THAT SUFFER MINIMAL CATASTROPHIC FORGETTINGTebogo Seipone, John A. BullinariaModeling Language, Cognition and Action · University of Birmingham
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.