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.

227 papers of 8,653 · showing 201–227Sort Recent · Most cited
  1. 2020
    Neural Topic Modeling with Continual Lifelong LearningPankaj Gupta, Yatin Chaudhary, Thomas A. Runkler, Schütze, HinrichICML · Siemens (Germany) · Technical University of Munich · +2
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  2. 2020
    Optimal Continual Learning has Perfect Memory and is NP-hardJeremias Knoblauch, Hisham Husain, Tom DietheICML · University of Warwick · The Alan Turing Institute · +2
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  3. 2021
    Variational Auto-Regressive Gaussian Processes for Continual LearningSanyam Kapoor, Theofanis Karaletsos, Thang D. BuiICML · Supélec · University of Applied Sciences and Arts of Southern Switzerland · +3
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  4. 2021
    Addressing Catastrophic Forgetting in Few-Shot ProblemsPauching Yap, Hippolyt Ritter, David BarberICML · University College London
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  5. 2021
    Federated Continual Learning with Weighted Inter-client TransferJaehong Yoon, Wonyong Jeong, Giwoong Lee … Sung Ju HwangICML · Korea Advanced Institute of Science and Technology · Korea Institute of Science and Technology
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  6. 2021
    Overcoming Catastrophic Forgetting by Bayesian Generative RegularizationPatrick H. Chen, Wei Wei, Cho‐Jui Hsieh, Bo DaiICML · University of California, Los Angeles
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  7. 2019
    Single-Net Continual Learning with Progressive Segmented TrainingXiaocong Du, Gouranga Charan, Frank Liu, Yu CaoICML · Arizona State University · Oak Ridge National Laboratory
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  8. 2019
    Frosting Weights for Better Continual TrainingXiaofeng Zhu, Feng Liu, Goce Trajcevski, Dingding WangICML · Northwestern University · Florida Atlantic University · +1
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  9. 2019
    Hierarchically Structured Meta-learningHuaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui LiICML · Pennsylvania State University
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  10. 2019PDF ↗
  11. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
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  12. 2019
    Policy Consolidation for Continual Reinforcement LearningChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
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  13. 2018
    Policy and Value Transfer in Lifelong Reinforcement LearningDavid Abel, Yuu Jinnai, Yue (Sophie) Guo … M. LittmanICML
  14. 2018
    Towards Robust Evaluations of Continual LearningSebastian Farquhar, Yarin GalICML · University of Oxford
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  15. 2018
    Progress & Compress : A scalable framework for continual learningJonathan Schwarz, Jelena Luketina, Wojciech Marian Czarnecki … Raia HadsellICML
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  16. 2018
    Differentiable plasticity: training plastic neural networks with backpropagationThomas Miconi, Jeff Clune, Kenneth O. StanleyICML · Neurosciences Institute · University of Wyoming · +1
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  17. 2018
    Continual Reinforcement Learning with Complex SynapsesChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
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  18. 2018
    Overcoming catastrophic forgetting with hard attention to the taskJoan Serrà, Dídac Surís, Marius Miron, Alexandros KaratzoglouICML
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  19. 2017
    Continual Learning Through Synaptic IntelligenceFriedemann Zenke, Ben Poole, S. GanguliICML
  20. 2017
    Meta NetworksTsendsuren Munkhdalai, Hong YuICML
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  21. 2017
    Differentiable Programs with Neural LibrariesAlexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel TarlowICML · Microsoft (United States)
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  22. 2015
    Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic ForgettingTomer Lancewicki, Benjamin Goodrich, Itamar ArelICML · University of Tennessee at Knoxville
  23. 2015
    Safe Policy Search for Lifelong Reinforcement Learning with Sublinear RegretHaitham Bou Ammar, Rasul Tutunov, Eric EatonICML · University of Pennsylvania
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  24. 2014
  25. 2014
    A PAC-Bayesian bound for Lifelong LearningAnastasia Pentina, Christoph H. LampertICML · Institute of Science and Technology Austria
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  26. 2013
  27. 2006
    A Class-Incremental Learning Method for Multi-Class Support Vector Machines in Text ClassificationBo-feng Zhang, Jinshu Su, Xin XuICML · National University of Defense Technology
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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.