mlsanityData to 14 Oct 2026

CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python

arXiv 2502.07432 ↗First posted 11 Feb 2025cs.LGcited by 2

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Abstract (arXiv)

CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured framework for real-time learning, supporting adaptive models that evolve over time. CapyMOA's architecture allows integration with frameworks such as MOA, scikit-learn and PyTorch, enabling the combination of high-performance online algorithms with modern deep learning techniques. By emphasizing efficiency, scalability, and usability, CapyMOA allows researchers and practitioners to tackle dynamic learning challenges across various domains. Website: https://capymoa.org. GitHub: https://github.com/adaptive-machine-learning/CapyMOA.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

5 of 12 references.

References · 5

  1. 2019Gradient based sample selection for online continual learningcited by 571
  2. 2020River: machine learning for streaming data in Pythoncited by 98
  3. 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
  4. 2023Avalanche: A PyTorch Library for Deep Continual Learningcited by 33
  5. 2023Adaptive Bernstein Change Detector for High-Dimensional Data Streamscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.