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.

13 papers of 4,574Sort Recent · Most cited
  1. 2023
    SketchOGD: Memory-Efficient Continual LearningYoungjae Min, Benjamin Wright, J. M. Bernstein, Navid AzizanIEEE Transactions · Massachusetts Institute of Technology
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  2. 2025
    CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental LearningJiangpeng He, Zhihao Duan, Fengqing ZhuCVPR · Massachusetts Institute of Technology · Purdue University West Lafayette
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  3. 2024
    Rapid context inference in a thalamocortical model using recurrent neural networksWei‐Long Zheng, Zhongxuan Wu, Ali Hummos … Michael M. HalassaNature Communications · Shanghai Jiao Tong University · Massachusetts Institute of Technology · +3
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  4. 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
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  5. 2021
    Lifelong Personalization via Gaussian Process Modeling for Long-Term HRISamuel Spaulding, Jocelyn Shen, Hae Won Park, Cynthia BreazealFrontiers · Massachusetts Institute of Technology
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  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. 2022
    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. 2019
    Toward an AI Physicist for Unsupervised LearningTailin Wu, Max TegmarkPhysical review. E · Theiss Research · Massachusetts Institute of Technology
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  9. 2020
    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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  10. 2019
    Continual Learning with Self-Organizing MapsPouya Bashivan, Martin Schrimpf, Robert Ajemian … Yuhai TuarXiv · Massachusetts Institute of Technology
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  11. 2019
    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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  12. 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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  13. 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
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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.