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.

8 papers of 11,817Sort Recent · Most cited
  1. 2021
    From Superficial to Deep: Language Bias driven Curriculum Learning for Visual Question AnsweringMingrui Lao, Yanming Guo, Yu Liu … Michael S. LewACM International Conference on Multimedia · Leiden University · National University of Defense Technology · +1
  2. 2021
    A Consciousness-Inspired Planning Agent for Model-Based Reinforcement LearningMingde Zhao, Zhen Liu, Sitao Luan … Yoshua BengioarXiv · Dalian University of Technology · Université de Montréal · +1
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  3. 2020
    Fast Sparse Connectivity Network Adaption via Meta-LearningBo Jin, Ke Cheng, Yue Qu … Xiaopeng WeiICDM · Dalian University of Technology · Dongbei University of Finance and Economics · +2
  4. 2020
    Novelty Detection and Online Learning for Chunk Data StreamsYi Wang, Yi Ding, Xiangjian He … Jiebo LuoTPAMI · Dalian University of Technology · University of Technology Sydney · +2
  5. 2019
    An Incremental Broad Learning Approach for Semi-Supervised ClassificationXize Liu, Tie Qiu, Chen Chen … Ning ChenIEEE Intl Conf on Dependable, Autonomic and Secure Comput… · Dalian University of Technology · Tianjin University · +2
  6. 2019
    A Fast Online Cascaded Regression Algorithm for Face AlignmentLin Feng, Caifeng Liu, Shenglan Liu, Huibing WangIEEE International Conference on Smart Internet of Things… · Dalian University of Technology · Dalian University · +1
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  7. 2020
    Recurrent Broad Learning Systems for Time Series PredictionMeiling Xu, Min Han, C. L. Philip Chen, Tie QiuIEEE Trans. Cybernetics · Dalian University of Technology · Dalian University · +2
  8. 2016
    Visual tracking via shallow and deep collaborative modelBohan Zhuang, Lijun Wang, Huchuan LuNeurocomputing · Dalian University of Technology
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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.