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.

10 papers of 11,817Sort Recent · Most cited
  1. 2022
    Edge Computation-in-Memory for In-situ Class-incremental Learning with Knowledge DistillationShinsei Yoshikiyo, Naoko Misawa, Chihiro Matsui, Ken TakeuchiIEEE International Symposium on Circuits and Systems (ISCAS) · Tokyo University of Information Sciences · The University of Tokyo
  2. 2021
    Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student NetworksHaruka Asanuma, Shiro Takagi, Yoshihiro Nagano … Masato OkadaJournal of the Physical Society of Japan · The University of Tokyo · University of Tsukuba · +1
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  3. 2020
    Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic ForgettingZeke Xie, Fengxiang He, Shaopeng Fu … Masashi SugiyamaNeural Computation · RIKEN Center for Advanced Intelligence Project · The University of Tokyo · +1
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  4. 2021
    Self-incremental learning vector quantization with human cognitive biasesNobuhito Manome, Shuji Shinohara, Tatsuji Takahashi … Ung‐il ChungScientific Reports · Tokyo University of Information Sciences · The University of Tokyo · +1
  5. 2021
    Developmental Robotics and its Role Towards Artificial General IntelligenceManfred Eppe, Stefan Wermter, Verena V. Hafner, Yukie NagaiKünstliche Intell. · Universität Hamburg · Humboldt-Universität zu Berlin · +1
  6. 2021
    Object Recognition with Continual Open Set Domain Adaptation for Home RobotIkki Kishida, Hong Chen, Masaki Baba … Hideki NakayamaWACV · The University of Tokyo
  7. 2019
    Decentralized Attention-based Personalized Human Mobility PredictionZipei Fan, Xuan Song, Renhe Jiang … Ryosuke ShibasakiACM on Interactive Mobile Wearable and Ubiquitous Technol… · Southern University of Science and Technology · The University of Tokyo
  8. 2019
    Personalized Food Image Classifier Considering Time-Dependent and Item-Dependent Food DistributionQing Yu, Masashi Anzawa, Sosuke Amano, Kiyoharu AizawaIEICE Transactions on Information and Systems · The University of Tokyo
  9. 2016
    A Joint Many-Task Model: Growing a Neural Network for Multiple NLP TasksKazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, Richard SocherEMNLP · Salesforce (United States) · The University of Tokyo
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  10. 2012
    On the use of Population Based Incremental Learning to do Reverse Engineering on Gene Regulatory NetworksLeon Palafox, Hitoshi IbaIEEE Congress on Evolutionary Computation · The University of Tokyo
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.