Omniglot data set for one-shot learning. The Omniglot data set is designed for developing more human-like learning algorithms. It contains 1623 different handwritten characters from 50 different alphabets. Each of the 1623 characters was drawn online via Amazon’s Mechanical Turk by 20 different people. Each image is paired with stroke data, a sequences of [x,y,t] coordinates with time (t) in milliseconds.

References in zbMATH (referenced in 24 articles )

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  1. Guo, Qian; Qian, Yuhua; Liang, Xinyan: GLRM: logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy (2022)
  2. Su, Yuling; Zhao, Hong; Lin, Yaojin: Few-shot learning based on hierarchical classification via multi-granularity relation networks (2022)
  3. Evans, Richard; Bošnjak, Matko; Buesing, Lars; Ellis, Kevin; Pfau, David; Kohli, Pushmeet; Sergot, Marek: Making sense of raw input (2021)
  4. Marchetti, Gionni; Patriarca, Marco; Heinsalu, Els: The role of bilinguals in the Bayesian naming game (2021)
  5. Müller, Heimo; Holzinger, Andreas: Kandinsky patterns (2021)
  6. Schoenholz, Samuel S.; Cubuk, Ekin D.: JAX, M.D. a framework for differentiable physics (2021)
  7. Wang, Li; Yan, Zhenya: Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (2021)
  8. Borisyak, Maxim; Ryzhikov, Artem; Ustyuzhanin, Andrey; Derkach, Denis; Ratnikov, Fedor; Mineeva, Olga: ((1 + \varepsilon))-class classification: an anomaly detection method for highly imbalanced or incomplete data sets (2020)
  9. Icard, Thomas F.: Calibrating generative models: the probabilistic Chomsky-Schützenberger hierarchy (2020)
  10. Lu, Zhixin; Bassett, Danielle S.: Invertible generalized synchronization: a putative mechanism for implicit learning in neural systems (2020)
  11. Marchetti, Gionni; Patriarca, Marco; Heinsalu, Els: A bird’s-eye view of naming game dynamics: from trait competition to Bayesian inference (2020)
  12. Yaohua Liu, Risheng Liu: BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning (2020) arXiv
  13. Ye, Han-Jia; Sheng, Xiang-Rong; Zhan, De-Chuan: Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach (2020)
  14. Castro, Daniel C.; Tan, Jeremy; Kainz, Bernhard; Konukoglu, Ender; Glocker, Ben: Morpho-MNIST: quantitative assessment and diagnostics for representation learning (2019)
  15. Liu, Zeyu; Yang, Yantao; Cai, Qingdong: Neural network as a function approximator and its application in solving differential equations (2019)
  16. Raissi, M.; Perdikaris, P.; Karniadakis, G. E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (2019)
  17. Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders: Computing programs for generalized planning using a classical planner (2019)
  18. Song, Yangqiu; Upadhyay, Shyam; Peng, Haoruo; Mayhew, Stephen; Roth, Dan: Toward any-language zero-shot topic classification of textual documents (2019)
  19. Telle, Jan Arne; Hernández-Orallo, José; Ferri, Cèsar: The teaching size: computable teachers and learners for universal languages (2019)
  20. Yang, Yibo; Perdikaris, Paris: Adversarial uncertainty quantification in physics-informed neural networks (2019)

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