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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.

27 papers of 11,817Sort Recent · Most cited
2009 Clear all
  1. 2009
    An Incremental Learning Method for Neural Networks Based on Sensitivity AnalysisBeatriz Pérez‐Sánchez, Óscar Fontenla-Romero, Bertha Guijarro‐BerdiñasSpringer LNCS · Universidade da Coruña
  2. 2009
    Chapter Seven. Continual Learning, Autonomy, and Competency among High School TeachersMeredith LordanUniversity of Toronto Press eBooks
  3. 2009
    Avoiding Catastrophic Forgetting by a Dual-Network Memory Model Using a Chaotic Neural NetworkM. HattoriPreprint
  4. 2009
    Fundamenty dydaktyki przekładu zorientowanej na procesPaweł KubiakRocznik Przekładoznawczy · Adam Mickiewicz University in Poznań
  5. 2009
    Developmentally regulated local inhibition and loss of plasticity hinder reinnervation of the skin by injured peripheral sensory axonsGeorgeann S. O’Brien, Seanna M. Martin, Christian Söllner … Alvaro SagastiCurrent Biology · University of California, Los Angeles · Wellcome Sanger Institute · +1
  6. 2009
    Noise Source Recognition Based on Two-Level Architecture Neural Network Ensemble for Incremental LearningGao Zhi-hua, Ben Kerong, Cui LilinEighth IEEE International Conference on Dependable, Auton… · Naval University of Engineering
  7. 2009
    The dark side of incremental learning: A model of cumulative semantic interference during lexical access in speech productionGary M. Oppenheim, Gary S. Dell, Myrna F. SchwartzCognition · University of Illinois Urbana-Champaign · Urbana University · +1
  8. 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
  9. 2009
    Self-organization of asymmetric associative networksChristian Albers, Klaus PawelzikBMC Neuroscience · University of Bremen
  10. 2009
    A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detectionWael Khreich, Éric Granger, Ali Miri, Robert SabourinIEEE Symposium on Computational Intelligence for Security… · École de Technologie Supérieure · University of Ottawa
  11. 2009
    The empowerment and sharing of knowledge among mental health service users: bridging the gap between the users and the mental health institutionPaz Flores, Eithne Leahy, Montse Sorro … C. MasferrerInternational Journal of Integrated Care · Hospital del Mar Research Institute · Hospital Del Mar
  12. 2009
    Does Research-Based Professional Development Make a Difference? A Longitudinal Investigation of Teacher Learning in Technology IntegrationChrystalla MouzaTeachers College Record The Voice of Scholarship in Educa… · University of Delaware
  13. 2009
    An incremental learning algorithm for supervised neural network with contour preserving classificationPiyabute Fuangkhon, Thitipong TanprasertInternational Conference on Electrical Engineering/Electr… · Assumption University
  14. 2009
    Adaptive Learning with Binary NeuronsJuan‐Manuel Torres‐Moreno, Mirta B. GordonarXiv
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  15. 2009
    Evolved Dual Weight Neural Architectures to Facilitate Incremental LearningJohn A. BullinariaInternational Joint Conference on Computational Intelligence · University of Birmingham
  16. 2009
    Capacity of Memory and Error Correction Capability in Chaotic Neural Networks with Incremental LearningToshinori Deguchi, Keisuke Matsuno, Toshiki Kimura, Naohiro IshiiStudies in computational intelligence · National Institute of Technology, Gifu College · Aichi Institute of Technology
  17. 2009
    Weights Updated Voting for Ensemble of Neural Networks Based Incremental LearningJianjun Liu, Shengping Xia, Weidong Hu, Wenxian YuSpringer LNCS · National University of Defense Technology
  18. 2009
    Modified Class-Incremental Generalized Discriminant AnalysisYunhui HeInternational Conference on Computer Engineering and Tech… · Nanjing University of Information Science and Technology
  19. 2009
    Structural plasticity and memory: Catastrophic forgetting, amnesia, and the spacing effectEdgar KörnerFrontiers
  20. 2009
    1P-207 Incremental learning by lattice neural networks(Neuronal Circuit & Information processing, The 47th Annual Meeting of the Biophysical Society of Japan)Daisuke Uragami, Hiroyuki OhtaSeibutsu Butsuri · Gakushuin University · National Defense Medical College
  21. 2009
    A Proposal For Continual Learning In RoboticsU. Nehmzow, L. RianoPreprint
  22. 2009
    A Survey of Research Work on Incremental Learning Based on Feedforward Neural NetworksWen-Xian YuPreprint
  23. 2009
    Meaningful Representations Prevent Catastrophic InterferenceJ. Bieger, I. Sprinkhuizen-Kuyper, I. V. van RooijPreprint
  24. 2009
    Accommodation of Characterization Tools: Understanding Vibration, Stray Electromagnetic Fields and Acoustics to Avoid Catastrophic Interferences in New Building Construction or Building RenovationL. YorkPreprint
  25. 2009
    Sharing in teams of heterogeneous, collaborative learning agentsChristopher M. Gifford, Arvin AgahInternational Journal of Intelligent Systems · University of Kansas · Center for Remote Sensing and Integrated Systems
  26. 2009
    Incremental learning of dynamic fuzzy neural networks for accurate system modelingXingsheng Deng, Xinzhou WangFuzzy Sets and Systems · Wuhan University · Changsha University of Science and Technology
  27. 2009
    Incremental learning of sequence patterns with a modular network modelIchiro Igari, Jun TaniNeurocomputing · RIKEN Center for Brain Science
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.