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.

21 papers of 11,817Sort Recent · Most cited
  1. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
  2. 2019
    Online Gaussian Process State-space Model: Learning and Planning for Partially Observable Dynamical SystemsSoon-Seo Park, Young-Jin Park, Young-Jae Min, Han‐Lim ChoiInternational Journal of Control Automation and Systems · NCSOFT (South Korea) · NAVER Cloud (South Korea) · +4
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  3. 2022
    Thalamocortical contribution to flexible learning in neural systemsMien Brabeeba Wang, Michael M. HalassaNetwork Neuroscience · Massachusetts Institute of Technology
  4. 2021
    Lifelong Personalization via Gaussian Process Modeling for Long-Term HRISamuel Spaulding, Jocelyn Shen, Hae Won Park, Cynthia BreazealFrontiers · Massachusetts Institute of Technology
  5. 2021
    Toward Robust and Efficient Online Adaptation for Deep Stereo Depth EstimationMilo Knowles, Valentin Peretroukhin, W. Nicholas Greene, Nicholas RoyICRA · Massachusetts Institute of Technology
  6. 2021
    Reproducibility Report: La-MAML: Look-ahead Meta Learning for Continual LearningJoel Joseph, Alex GuarXiv · Indian Institute of Technology BHU · Banaras Hindu University · +1
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  7. 2020
    Energy-Based Models for Continual LearningShuang Li, Yilun Du, Gido M. van de Ven, Igor MordatchCoLLAs · Massachusetts Institute of Technology
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  8. 2020
    Lethean Attack: An Online Data Poisoning TechniqueEyal PerryarXiv · Massachusetts Institute of Technology
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  9. 2019
    Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion PlannersAhmed H. Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. YipIEEE Transactions · University of California San Diego · Massachusetts Institute of Technology
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  10. 2020
    An Efficient ADMM-Aided Deep Learning-Based Signal Detector for Uplink Massive MIMOHongji Huang, J.M. Cioffi, Seyyed Ali HashemiIEEE International Conference on Communications Workshops… · Massachusetts Institute of Technology · Stanford University
  11. 2020
    Accelerated Learning with Robustness to Adversarial RegressorsJoseph E. Gaudio, Anuradha M. Annaswamy, José M. Moreu … Travis E. GibsonConference on Learning for Dynamics & Control · Massachusetts Institute of Technology · Harvard University Press
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  12. 2018
    Toward an AI Physicist for Unsupervised LearningTailin Wu, Max TegmarkPhysical review. E · Theiss Research · Massachusetts Institute of Technology
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  13. 2019
    LAMOL: LAnguage MOdeling for Lifelong Language LearningFan-Keng Sun, Cheng-Hao Ho, Hung-yi LeeICLR · Massachusetts Institute of Technology · National Taiwan University
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  14. 2019
    Autoencoder-Based Incremental Class Learning without Retraining on Old DataEuntae Choi, Kyungmi Lee, Ki‐Young ChoiarXiv · Seoul National University · Massachusetts Institute of Technology
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  15. 2019
    Continual Learning with Self-Organizing MapsPouya Bashivan, Martin Schrimpf, Robert Ajemian … Yuhai TuarXiv · Massachusetts Institute of Technology
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  16. 2018
    Learning to Learn without Forgetting By Maximizing Transfer and Minimizing InterferenceMatthew Riemer, Ignacio Cases, Robert Ajemian … Gerald TesauroICLR · IBM (United States) · Stanford University · +2
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  17. 2018
    Distributed Weight Consolidation: A Brain Segmentation Case StudyPatrick McClure, Charles Zheng, Jakub Kaczmarzyk … Francisco PereiraNeurIPS · National Institutes of Health · Massachusetts Institute of Technology
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  18. 2017
    Habituation based synaptic plasticity and organismic learning in a quantum perovskiteFan Zuo, Priyadarshini Panda, Michele Kotiuga … Shriram RamanathanNature Communications · Purdue University West Lafayette · Rutgers, The State University of New Jersey · +3
  19. 2012
    Semantic categorization of outdoor scenes with uncertainty estimates using multi-class gaussian process classificationRohan Paul, Rudolph Triebel, Daniela Rus, Paul NewmanIROS · Oxford Research Group · University of Oxford · +1
  20. 2009
    CA3 NMDA Receptors are Required for the Rapid Formation of a Salient Contextual RepresentationThomas J. McHugh, Susumu TonegawaHippocampus · Howard Hughes Medical Institute · McGovern Institute for Brain Research · +1
  21. 2008
    Dynamic visual category learningTom Yeh, Trevor DarrellCVPR · Massachusetts Institute of Technology · University of California, Berkeley
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.