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.

7 papers of 6,984Sort Recent · Most cited
  1. 2022
    Self-Activating Neural Ensembles for Continual Reinforcement LearningSam Powers, Xing, Eliot, Gupta, AbhinavCoLLAs · Carnegie Mellon University · Meta (Israel)
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  2. 2022
    Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR ModelsYuki Takashima, Shota Horiguchi, Shinji Watanabe … Yohei KawaguchiInterspeech · Hitachi (Japan) · Johns Hopkins University · +1
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  3. 2022
    Rethinking Architecture Design for Tackling Data Heterogeneity in Federated LearningLiangqiong Qu, Yuyin Zhou, Paul Pu Liang … Daniel L. RubinCVPR · Stanford University · University of California, Santa Cruz · +2
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  4. 2022
    Lifelong Graph LearningChen Wang, Yuheng Qiu, Dasong Gao, Sebastian SchererCVPR · Carnegie Mellon University
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  5. 2022
    Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic ManipulationJacky Liang, Mohit Sharma, Alex LaGrassa … Oliver KroemerICRA · Carnegie Mellon University
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  6. 2022
    AirLoop: Lifelong Loop Closure DetectionDasong Gao, Chen Wang, Sebastian SchererICRA · Carnegie Mellon University
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  7. 2022
    Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic EnvironmentsAbhiram Iyer, Karan Grewal, Akash Velu … Subutai AhmadFrontiers · Carnegie Mellon University · Stanford University · +1
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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.