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.

167 papers of 4,574 · showing 151–167Sort Recent · Most cited
  1. 2019
    Marginal Replay vs Conditional Replay for Continual LearningTimothée Lesort, Alexander Gepperth, Andrei Stoian, David FilliatSpringer LNCS · École Nationale Supérieure de Techniques Avancées · Thales (France) · +1
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  2. 2019
    A Progressive Model to Enable Continual Learning for Semantic Slot FillingYilin Shen, Xiangyu Zeng, Hongxia JinEMNLP · Samsung (South Korea) · Samsung (United States) · +1
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  3. 2019
    Meta-Learning Improves Lifelong Relation ExtractionAbiola Obamuyide, Andreas VlachosWorkshop on Representation Learning for NLP (RepL4NLP-2019) · University of Cambridge · PRG S&Tech (South Korea) · +1
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  4. 2019
    Continual Learning with Deep ArchitecturesVincenzo LomonacoAMS Dottorato Institutional Doctoral Theses Repository (U…
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  5. 2019
    Incremental Learning from Scratch for Task-Oriented Dialogue SystemsWeikang Wang, Jiajun Zhang, Qian Li … Zhifei LiACL · Shandong Institute of Automation · Institute of Automation · +3
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  6. 2019
    Continuous Learning for Large-scale Personalized Domain ClassificationHan Li, Jihwan Lee, Sidharth Mudgal … Young‐Bum KimNAACL · University of Wisconsin–Madison · Amazon (Germany)
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  7. 2019
    Lifelong Learning Starting From ZeroClaes Strannegård, Herman Carlström, Niklas Engsner … Morteza Haghir ChehreghaniSpringer LNCS · Chalmers University of Technology
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  8. 2019
    Transfer Learning with Sparse Associative MemoriesQuentin Jodelet, Vincent Gripon, Masafumi HagiwaraSpringer LNCS · Keio University · IMT Atlantique
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  9. 2019
    Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RLAnusha Nagabandi, Chelsea Finn, Sergey LevineICLR · University of California, Berkeley
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  10. 2019
    An Empirical Study of Example Forgetting during Deep Neural Network LearningMariya Toneva, Alessandro Sordoni, Rémi Tachet des Combes … Geoffrey J. GordonICLR · Carnegie Mellon University · Microsoft (United States) · +1
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  11. 2019
    Efficient Lifelong Learning with A-GEMArslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed ElhoseinyICLR
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  12. 2019
    Experience Replay for Continual LearningDavid Rolnick, Arun Ahuja, Jonathan Schwarz … Greg WayneNeurIPS · California University of Pennsylvania · University of Pennsylvania · +1
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  13. 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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  14. 2019
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
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  15. 2019
    Measuring and regularizing networks in function spaceAri S. Benjamin, David Rolnick, Konrad P. KördingICLR · University of Pennsylvania · Philadelphia University
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  16. 2019
    Continual Lifelong Learning with Neural Networks: A ReviewG. I. Parisi, Ronald Kemker, Jose L. Part … Stefan WermterNeural Networks
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  17. 2019
    A Reinforcement Learning Architecture That Transfers Knowledge Between Skills When Solving Multiple TasksPaolo Tommasino, Daniele Caligiore, Marco Mirolli, Gianluca BaldassarreIEEE TCDS · Nanyang Technological University · Institute of Cognitive Sciences and Technologies
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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.