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.

9 papers of 11,817Sort Recent · Most cited
  1. 2021
    Lifelong learning on evolving graphs under the constraints of imbalanced classes and new classesLukas Galke, Iacopo Vagliano, Benedikt Franke … Ansgar ScherpNeural Networks · Max Planck Institute for Psycholinguistics · Amsterdam University Medical Centers · +3
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  2. 2021
    TaskDrop: A Competitive Baseline for Continual Learning of Sentiment ClassificationJian-Ping Mei, Yilun Zhen, Qianwei Zhou, Rui YanNeural Networks · Zhejiang University of Science and Technology · Zhejiang University of Technology
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  3. 2021
    Continuous learning of spiking networks trained with local rulesDmitry Antonov, Kirill Sviatov, Sergey SukhovNeural Networks · Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences · Ulyanovsk State Technical University
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  4. 2021
    Online Lifelong Generalized Zero-Shot LearningChandan Gautam, Sethupathy Parameswaran, Ashish Mishra, Suresh SundaramNeural Networks · Agency for Science, Technology and Research · Institute for Infocomm Research · +2
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  5. 2021
    Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization NetworksKrishnakumar Santhakumar, Hamidreza KasaeiNeural Networks · University of Groningen
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  6. 2021
    Deep Bayesian Unsupervised Lifelong LearningTingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer DyNeural Networks · Bryant University · Bryan College · +1
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  7. 2021
    Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble MethodsJary Pomponi, Simone Scardapane, Aurelio UnciniNeural Networks · Sapienza University of Rome
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  8. 2021
    Schematic Memory Persistence and Transience for Efficient and Robust Continual LearningYuyang Gao, Giorgio A. Ascoli, Liang ZhaoNeural Networks · Emory University · George Mason University · +2
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  9. 2021
    Continual Learning for Recurrent Neural Networks: an Empirical EvaluationAndrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide BacciuNeural Networks · University of Pisa · Scuola Normale Superiore · +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 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.