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.

19 papers of 11,817Sort Recent · Most cited
  1. 2022
    Memory Bounds for Continual LearningXi Chen, Christos H. Papadimitriou, Binghui PengIEEE 63rd Annual Symposium on Foundations of Computer Sci… · Columbia University
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  2. 2022
    Learning to Learn and Remember Super Long Multi-Domain Task SequenceZhenyi Wang, Li Shen, Tiehang Duan … Mingchen GaoCVPR · University at Buffalo, State University of New York · Jingdong (China) · +1
  3. 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
  4. 2022
    Fine-tuned Language Models are Continual LearnersThomas Scialom, Tuhin Chakrabarty, Smaranda MuresanEMNLP · University of Missouri · Columbia University
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  5. 2022
    Learning Representations for New Sound Classes With Continual Self-Supervised LearningZhepei Wang, Cem Subakan, Xilin Jiang … Paris SmaragdisIEEE Signal Processing Letters · University of Illinois Urbana-Champaign · Concordia University · +2
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  6. 2021
    Meta-learning synaptic plasticity and memory addressing for continual familiarity detectionDanil Tyulmankov, Guangyu Robert Yang, L. F. AbbottNeuron · Columbia University · Allen Institute for Brain Science · +1
  7. 2021
    Meta-learning local synaptic plasticity for continual familiarity detectionDanil Tyulmankov, Guangyu Robert Yang, L. F. AbbottbioRxiv · Columbia University
  8. 2020
    Continual Learning in Task-Oriented Dialogue SystemsAndrea Madotto, Zhaojiang Lin, Zhenpeng Zhou … Zhiguang WangEMNLP · Hong Kong University of Science and Technology · Meta (Israel) · +1
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  9. 2020
    Maximizing BCI Human Feedback using Active LearningZizhao Wang, Junyao Shi, Iretiayo Akinola, Peter AllenIROS · Columbia University
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  10. 2020
    Learning to Learn with Feedback and Local PlasticityJack Lindsey, Ashok Litwin-KumarNeurIPS · Columbia University
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  11. 2019
    A Story of Two Streams: Reinforcement Learning Models from Human Behavior and NeuropsychiatryBaihan Lin, Guillermo Cecchi, Djallel Bouneffouf … Irina RishAdaptive Agents and Multi-Agents Systems · Columbia University · IBM (United States) · +1
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  12. 2019
    Model primitives for hierarchical lifelong reinforcement learningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferAutonomous Agents and Multi-Agent Systems · Columbia University · Stanford University
  13. 2019
    Continual Learning in a Multi-Layer Network of an Electric FishSalomon Z. Muller, Abigail N Zadina, L. F. Abbott, Nathaniel B. SawtellCell · Columbia University
  14. 2019
    Modulating the Use of Multiple Memory Systems in Value-based Decisions with Contextual NoveltyKatherine Duncan, Annika Semmler, Daphna ShohamyJournal of Cognitive Neuroscience · University of Toronto · Vrije Universiteit Amsterdam · +1
  15. 2019
    Task representations in neural networks trained to perform many cognitive tasksGuangyu Robert Yang, Madhura R. Joglekar, Hui Song … Xiao‐Jing WangNature Neuroscience · New York University · Columbia University · +5
  16. 2019
    A Progressive Model to Enable Continual Learning for Semantic Slot FillingYilin Shen, Xiangyu Zeng, Hongxia JinEMNLP · Samsung (South Korea) · Samsung (United States) · +1
  17. 2018
    Low-shot Learning via Covariance-Preserving Adversarial Augmentation NetworksHang Gao, Zheng Shou, Alireza Zareian … Shih‐Fu ChangNeurIPS · Shanghai Jiao Tong University · Columbia University
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  18. 2018
    High-Capacity Fingerprint Recognition System based on a Dynamic Memory-Capacity Estimation TechniquePavan Kumar Chundi, Ajay Kumar Sridhar, Saarthak Sarup, Mingoo SeokIEEE Biomedical Circuits and Systems Conference (BioCAS) · Columbia University
  19. 2017
    Reminders of past choices bias decisions for reward in humansAaron M. Bornstein, Mel Win Khaw, Daphna Shohamy, Nathaniel D. DawNature Communications · Princeton University · Columbia University · +2
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.