Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4,574 papers · showing 4551–4574Sort Recent · Most cited
  1. 2015PDF ↗
  2. 2016
    Net2Net: Accelerating Learning via Knowledge TransferTianqi Chen, Ian Goodfellow, Jonathon ShlensICLR · University of Washington · Google (United States)
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  3. 2015
    Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer StrategySang-Woo Lee, Min-Oh Heo, Jiwon Kim … Byoung‐Tak ZhangarXiv · Seoul National University
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  4. 2015PDF ↗
  5. 2015
    Safe Policy Search for Lifelong Reinforcement Learning with Sublinear RegretHaitham Bou Ammar, Rasul Tutunov, Eric EatonICML · University of Pennsylvania
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  6. 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
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  7. 2015
    Lifelong Machine Learning for Topic Modeling and BeyondZhiyuan ChenNAACL · University of Illinois Chicago
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  8. 2015PDF ↗
  9. 2014
    Learning to see like children: proof of conceptMarco Gori, Marco Lippi, Marco Maggini, Stefano MelacciarXiv
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  10. 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
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  11. 2014
    Representation as a ServiceOuais Alsharif, Philip Bachman, Joëlle PineauarXiv
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  12. 2014
    An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural NetworksIan Goodfellow, Mehdi Mirza, Xiao Da … Yoshua BengioICLR · Département d'Informatique · Université de Montréal · +1
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  13. 2013
    Clustering Markov Decision Processes For Continual TransferMaqsood Mahmud, Majd Hawasly, Benjamin Rosman, Subramanian RamamoorthyarXiv
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  14. 2014
    A PAC-Bayesian bound for Lifelong LearningAnastasia Pentina, Christoph H. LampertICML · Institute of Science and Technology Austria
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  15. 2013
    Sequential Transfer in Multi-armed Bandit with Finite Set of ModelsMohammad Gheshlaghi Azar, Alessandro Lazaric, Emma BrunskillNeurIPS · Carnegie Mellon University · Laboratoire d'Informatique Fondamentale de Lille
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  16. 2013
    Active Task Selection for Lifelong Machine LearningPaul Ruvolo, Eric EatonAAAI · Bryn Mawr College
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  17. 2013
    The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effectsMartial Mermillod, Aurélia Bugaïska, Patrick BoninFrontiers · Centre National de la Recherche Scientifique · Institut Universitaire de France · +3
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  18. 2013
    Sleep-Dependent Synaptic Down-Selection (II): Single-Neuron Level Benefits for Matching, Selectivity, and SpecificityAtif Hashmi, Andrew Nere, Giulio TononiFrontiers · University of Wisconsin–Madison
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  19. 2013
    Incremental learning of skill collections based on intrinsic motivationJan Hendrik Metzen, Frank KirchnerFrontiers · University of Bremen · German Research Centre for Artificial Intelligence
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  20. 2007PDF ↗
  21. 2007
    Incremental learning based on extraction of action sequence for autonomous mobile robotSatoshi Ohno, Kazuo NakazawaTRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS… · Keio University
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  22. 2005
    Hebbian learning rule restraining catastrophic forgetting in pulse neural networkMakoto Motoki, Tomoki Hamagami, Seiichi Koakutsu, Hironori HirataElectrical Engineering in Japan · Chiba University
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  23. 1991
    Incremental learning with rule-based neural networksCharles M. Higgins, R.M. GoodmanIJCNN · California Institute of Technology
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  24. 1998
    Learning to LearnSebastian Thrun, Lorien PrattSpringer · Australian National University · Carnegie Mellon University
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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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.