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 85 articles )

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  1. Clark, Stephen R.: Unifying neural-network quantum states and correlator product states via tensor networks (2018)
  2. Fang, Le-Heng; Lin, Wei; Luo, Qiang: Brain-inspired constructive learning algorithms with evolutionally additive nonlinear neurons (2018)
  3. Haber, Eldad; Ruthotto, Lars: Stable architectures for deep neural networks (2018)
  4. Jacobs, Matt; Merkurjev, Ekaterina; Esedoḡlu, Selim: Auction dynamics: a volume constrained MBO scheme (2018)
  5. Louart, Cosme; Liao, Zhenyu; Couillet, Romain: A random matrix approach to neural networks (2018)
  6. Mokhtari, Aryan; Eisen, Mark; Ribeiro, Alejandro: IQN: an incremental quasi-Newton method with local superlinear convergence rate (2018)
  7. Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec: geomstats: a Python Package for Riemannian Geometry in Machine Learning (2018) arXiv
  8. Sagun, Levent; Trogdon, Thomas; LeCun, Yann: Universal halting times in optimization and machine learning (2018)
  9. Shi, Zuoqiang; Sun, Jian; Tian, Minghao: Harmonic extension on the point cloud (2018)
  10. Agarwal, Naman; Bullins, Brian; Hazan, Elad: Second-order stochastic optimization for machine learning in linear time (2017)
  11. Andersen, Michael Riis; Vehtari, Aki; Winther, Ole; Hansen, Lars Kai: Bayesian inference for spatio-temporal spike-and-slab priors (2017)
  12. Bae, Egil; Merkurjev, Ekaterina: Convex variational methods on graphs for multiclass segmentation of high-dimensional data and point clouds (2017)
  13. Charisopoulos, Vasileios; Maragos, Petros: Morphological perceptrons: geometry and training algorithms (2017)
  14. Cloninger, Alexander; Czaja, Wojciech; Doster, Timothy: The pre-image problem for Laplacian eigenmaps utilizing $L_1$ regularization with applications to data fusion (2017)
  15. Ding, Chao; Qi, Hou-Duo: Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction (2017)
  16. Dong, Bin: Sparse representation on graphs by tight wavelet frames and applications (2017)
  17. Elmoataz, Abderrahim; Lozes, François; Toutain, Matthieu: Nonlocal PDEs on graphs: from Tug-of-War games to unified interpolation on images and point clouds (2017)
  18. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  19. Haojin Yang, Martin Fritzsche, Christian Bartz, Christoph Meinel: BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet (2017) arXiv
  20. Heckel, Reinhard; Tschannen, Michael; Bölcskei, Helmut: Dimensionality-reduced subspace clustering (2017)

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