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.

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2022 Nature Communications Clear all

2022
Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networksTimothy Tadros, Giri P. Krishnan, Ramyaa Ramyaa, Maxim BazhenovNature Communications · University of California San Diego · New Mexico Institute of Mining and Technology
2022
Experimentally validated memristive memory augmented neural network with efficient hashing and similarity searchRuibin Mao, Bo Wen, Arman Kazemi … Can LiNature Communications · University of Hong Kong · University of Notre Dame · +5
2022
A framework for the general design and computation of hybrid neural networksRong Zhao, Zheyu Yang, Hao Zheng … Luping ShiNature Communications · Chinese Institute for Brain Research · Tsinghua University · +1
2022
EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle pickingXinyu Zhang, Tian-Fang Zhao, Jiansheng Chen … Xueming LiNature Communications · Tsinghua University · University of Science and Technology Beijing · +2
2022
Introducing principles of synaptic integration in the optimization of deep neural networksGiorgia Dellaferrera, Stanisław Woźniak, Giacomo Indiveri … Evangelos EleftheriouNature Communications · University of Zurich · IBM Research - Zurich · +3

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.