MNIST

THE MNIST DATABASE of handwritten digits. The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.


References in zbMATH (referenced in 233 articles )

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  1. Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, Alaa Eddine Belfedhal: TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption (2021) arXiv
  2. Baskerville, Nicholas P.; Keating, Jonathan P.; Mezzadri, Francesco; Najnudel, Joseph: The loss surfaces of neural networks with general activation functions (2021)
  3. Chzhen, Evgenii; Denis, Christophe; Hebiri, Mohamed: Minimax semi-supervised set-valued approach to multi-class classification (2021)
  4. Cloninger, A.; Mhaskar, H. N.: Cautious active clustering (2021)
  5. De Loera, Jesús A.; Haddock, Jamie; Ma, Anna; Needell, Deanna: Data-driven algorithm selection and tuning in optimization and signal processing (2021)
  6. Frye, Charles G.; Simon, James; Wadia, Neha S.; Ligeralde, Andrew; Deweese, Michael R.; Bouchard, Kristofer E.: Critical point-finding methods reveal gradient-flat regions of deep network losses (2021)
  7. Geiger, Mario; Petrini, Leonardo; Wyart, Matthieu: Landscape and training regimes in deep learning (2021)
  8. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  9. Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs: PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python (2021) arXiv
  10. Hao, Zhonghua; Ma, Shiwei; Chen, Hui; Liu, Jingjing: Dataset denoising based on manifold assumption (2021)
  11. Iwen, Mark A.; Krahmer, Felix; Krause-Solberg, Sara; Maly, Johannes: On recovery guarantees for one-bit compressed sensing on manifolds (2021)
  12. Kovachki, Nikola B.; Stuart, Andrew M.: Continuous time analysis of momentum methods (2021)
  13. Mai, Xiaoyi; Couillet, Romain: Consistent semi-supervised graph regularization for high dimensional data (2021)
  14. Ma, Yu; Wang, Shafei; Yang, Junan; Bao, Yanfei; Yang, Jian: An implicit memory-based method for supervised pattern recognition (2021)
  15. Metel, Michael R.; Takeda, Akiko: Stochastic proximal methods for non-smooth non-convex constrained sparse optimization (2021)
  16. Nielsen, Frank; Sun, Ke: Chain rule optimal transport (2021)
  17. Northcutt, Curtis G.; Jiang, Lu; Chuang, Isaac L.: Confident learning: estimating uncertainty in dataset labels (2021)
  18. Peterfreund, Erez; Gavish, Matan: Multidimensional scaling of noisy high dimensional data (2021)
  19. Qin, Shanshan; Mudur, Nayantara; Pehlevan, Cengiz: Contrastive similarity matching for supervised learning (2021)
  20. Singh, Rishabh; Principe, Jose C.: Toward a kernel-based uncertainty decomposition framework for data and models (2021)

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