mlsanityData to 7 Oct 2026

Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more

arXiv 1912.08165 ↗First posted 17 Dec 2019stat.ML cs.LGcited by 16

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Abstract (arXiv)

Cyanure is an open-source C++ software package with a Python interface. The goal of Cyanure is to provide state-of-the-art solvers for learning linear models, based on stochastic variance-reduced stochastic optimization with acceleration mechanisms. Cyanure can handle a large variety of loss functions (logistic, square, squared hinge, multinomial logistic) and regularization functions (l_2, l_1, elastic-net, fused Lasso, multi-task group Lasso). It provides a simple Python API, which is very close to that of scikit-learn, which should be extended to other languages such as R or Matlab in a near future.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 5; 2024: 4.

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  1. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  2. 2022Masked Siamese Networks for Label-Efficient Learningcited by 301
  3. 2022Siamese Image Modeling for Self-Supervised Vision Representation Learningcited by 78
  4. 2023Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labelscited by 37
  5. 2022Benchopt: Reproducible, efficient and collaborative optimization benchmarkscited by 27
  6. 2024MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representationscited by 19
  7. 2022Light-weight probing of unsupervised representations for Reinforcement Learningcited by 15
  8. 2023Contrastive Tuning: A Little Help to Make Masked Autoencoders Forgetcited by 15
  9. 2023MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignmentscited by 9
  10. 2024Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Featurescited by 6
  11. 2024Differentially Private Representation Learning via Image Captioningcited by 5
  12. 2024No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representationscited by 5
4 more
  1. 2021Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularizationcited by 1
  2. 2023Benchmarking Low-Shot Robustness to Natural Distribution Shiftscited by 1
  3. 2021Complexity of Inexact Proximal Point Algorithm for minimizing convex functions with Holderian Growthcited by 0
  4. 2023On Inductive Biases for Machine Learning in Data Constrained Settingscited by 0