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 203 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. Cloninger, A.; Mhaskar, H. N.: Cautious active clustering (2021)
  3. 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
  4. Hao, Zhonghua; Ma, Shiwei; Chen, Hui; Liu, Jingjing: Dataset denoising based on manifold assumption (2021)
  5. Iwen, Mark A.; Krahmer, Felix; Krause-Solberg, Sara; Maly, Johannes: On recovery guarantees for one-bit compressed sensing on manifolds (2021)
  6. Northcutt, Curtis G.; Jiang, Lu; Chuang, Isaac L.: Confident learning: estimating uncertainty in dataset labels (2021)
  7. Peterfreund, Erez; Gavish, Matan: Multidimensional scaling of noisy high dimensional data (2021)
  8. Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni: Avalanche: an End-to-End Library for Continual Learning (2021) arXiv
  9. Xiao, Chuanfu; Yang, Chao; Li, Min: Efficient alternating least squares algorithms for low multilinear rank approximation of tensors (2021)
  10. Zhou, Bai-cun; Han, Cong-ying; Guo, Tian-de: Convergence of stochastic gradient descent in deep neural network (2021)
  11. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  12. Abin, Ahmad Ali; Bashiri, Mohammad Ali; Beigy, Hamid: Learning a metric when clustering data points in the presence of constraints (2020)
  13. Bauvin, Baptiste; Capponi, Cécile; Roy, Jean-Francis; Laviolette, François: Fast greedy (\mathcalC)-bound minimization with guarantees (2020)
  14. Bellavia, Stefania; Krejić, Nataša; Morini, Benedetta: Inexact restoration with subsampled trust-region methods for finite-sum minimization (2020)
  15. Boutin, Victor; Franciosini, Angelo; Ruffier, Franck; Perrinet, Laurent: Effect of top-down connections in hierarchical sparse coding (2020)
  16. Carlsson, Gunnar; Gabrielsson, Rickard Brüel: Topological approaches to deep learning (2020)
  17. Challa, Aditya; Danda, Sravan; Sagar, B. S. Daya; Najman, Laurent: Power spectral clustering (2020)
  18. Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr: FedML: A Research Library and Benchmark for Federated Machine Learning (2020) arXiv
  19. Dong, Bin; Ju, Haocheng; Lu, Yiping; Shi, Zuoqiang: CURE: curvature regularization for missing data recovery (2020)
  20. Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)

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