Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 6,984Sort Recent · Most cited
  1. 2026
    In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based RegularizationCooper Simpson, Stephen Becker, Alireza DoostanJournal of Computational Physics · University of Colorado Boulder · University of Washington Applied Physics Laboratory · +2
    PDF ↗
  2. 2024
    Canonical Shape Projection Is All You Need for 3D Few-Shot Class Incremental LearningAli Cheraghian, Zeeshan Hayder, Sameera Ramasinghe … Mehrtash HarandiECCV · Australian National University · Commonwealth Scientific and Industrial Research Organisation · +6
  3. 2023
    TKIL: Tangent Kernel Optimization for Class Balanced Incremental LearningJinlin Xiang, Eli ShlizermanICCV · University of Washington · Seattle University
  4. 2020
    Learning to Solve NLP Tasks in an Incremental Number of LanguagesGiuseppe Castellucci, Simone Filice, Danilo Croce, Roberto BasiliACL · Amazon (United States) · Seattle University · +3
  5. 2020
    An Empirical Investigation towards Efficient Multi-Domain Language Model Pre-trainingKristjan Arumae, Qing Sun, Parminder BhatiaEMNLP · Amazon (United States) · Seattle University · +1
    PDF ↗
  6. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
    PDF ↗
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.