Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features tight integration with numpy, transparent use of a GPU, efficient symbolic differentiation, speed and stability optimizations, dynamic C code generation, and extensive unit-testing and self-verification. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal). (Source:

References in zbMATH (referenced in 59 articles )

Showing results 1 to 20 of 59.
Sorted by year (citations)

1 2 3 next

  1. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  2. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  3. Hottung, André; Tanaka, Shunji; Tierney, Kevin: Deep learning assisted heuristic tree search for the container pre-marshalling problem (2020)
  4. Hughes, Mark C.: A neural network approach to predicting and computing knot invariants (2020)
  5. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  6. Sun, Luning; Gao, Han; Pan, Shaowu; Wang, Jian-Xun: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2020)
  7. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  8. Willmott, Devin; Murrugarra, David; Ye, Qiang: Improving RNA secondary structure prediction via state inference with deep recurrent neural networks (2020)
  9. Arnaudon, Alexis; Holm, Darryl D.; Sommer, Stefan: A geometric framework for stochastic shape analysis (2019)
  10. Bonilla, Edwin V.; Krauth, Karl; Dezfouli, Amir: Generic inference in latent Gaussian process models (2019)
  11. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  12. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  13. Kühnel, Line; Sommer, Stefan; Arnaudon, Alexis: Differential geometry and stochastic dynamics with deep learning numerics (2019)
  14. Livezey, Jesse A.; Bujan, Alejandro F.; Sommer, Friedrich T.: Learning overcomplete, low coherence dictionaries with linear inference (2019)
  15. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  16. Wang, Bao; Yin, Penghang; Bertozzi, Andrea Louise; Brantingham, P. Jeffrey; Osher, Stanley Joel; Xin, Jack: Deep learning for real-time crime forecasting and its ternarization (2019)
  17. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  18. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  19. Andrew Beers; James Brown; Ken Chang; Katharina Hoebel; Elizabeth Gerstner; Bruce Rosen; Jayashree Kalpathy-Cramer: DeepNeuro: an open-source deep learning toolbox for neuroimaging (2018) arXiv
  20. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)

1 2 3 next