Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2025
    Lifelong Evolution of SwarmsLorenzo Leuzzi, Davide Bacciu, Sabine Hauert … Andrea CossuGenetic and Evolutionary Computation Conference · University of Pisa · University of Cagliari · +1
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  2. 2020
    Hidden Markov Neural NetworksLorenzo Rimella, Nick WhiteleyEntropy · Collegio Carlo Alberto · University of Turin · +1
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  3. 2024
    AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental LearningPengxiang Wang, Hongbo Bo, Jun Hong … Kedian MuSpringer LNCS · Peking University · University of Bristol · +2
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  4. 2023
    Multi-lingual agents through multi-headed neural networksJonathan D. Thomas, Ra ́ul Santos-Rodr ́ıguez, Mihai Anca, Robert J. PiechockiNorthern Lights Deep Learning Workshop · University of Bristol
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  5. 2022
    Ego-graph Replay based Continual Learning for Misinformation Engagement PredictionHongbo Bo, Ryan McConville, Jun Hong, Weiru LiuIJCNN · University of Bristol · University of the West of England
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  6. 2021
    CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future DirectionsVincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodríguez … Davide MaltoniArtificial Intelligence · University of Bologna · Mila - Quebec Artificial Intelligence Institute · +7
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  7. 2021
    Learning offline: memory replay in biological and artificial reinforcement learningEmma L. Roscow, Raymond Chua, Rui Ponte Costa … Nathan F. LeporaTrends in Neurosciences · Centre de Recerca Matemàtica · McGill University · +2
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  8. 2019
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein GradientsYu Chen, Tom Diethe, Neil D. LawrencearXiv · University of Bristol · Amazon (Germany)
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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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.