Theano

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: http://freecode.com/)


References in zbMATH (referenced in 64 articles )

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  1. Alvaro Tejero-Canteroe; Jan Boeltse; Michael Deistlere; Jan-Matthis Lueckmanne; Conor Durkane; Pedro J. Gonçalves; David S. Greenberg; Jakob H. Macke: sbi: A toolkit for simulation-based inference (2020) not zbMATH
  2. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  3. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  4. Hottung, André; Tanaka, Shunji; Tierney, Kevin: Deep learning assisted heuristic tree search for the container pre-marshalling problem (2020)
  5. Hughes, Mark C.: A neural network approach to predicting and computing knot invariants (2020)
  6. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  7. Sun, Luning; Gao, Han; Pan, Shaowu; Wang, Jian-Xun: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2020)
  8. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  9. Willmott, Devin; Murrugarra, David; Ye, Qiang: Improving RNA secondary structure prediction via state inference with deep recurrent neural networks (2020)
  10. Arnaudon, Alexis; Holm, Darryl D.; Sommer, Stefan: A geometric framework for stochastic shape analysis (2019)
  11. Bonilla, Edwin V.; Krauth, Karl; Dezfouli, Amir: Generic inference in latent Gaussian process models (2019)
  12. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  13. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  14. Higham, Catherine F.; Higham, Desmond J.: Deep learning: an introduction for applied mathematicians (2019)
  15. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  16. Kühnel, Line; Sommer, Stefan; Arnaudon, Alexis: Differential geometry and stochastic dynamics with deep learning numerics (2019)
  17. Livezey, Jesse A.; Bujan, Alejandro F.; Sommer, Friedrich T.: Learning overcomplete, low coherence dictionaries with linear inference (2019)
  18. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  19. Sommer, Stefan: An infinitesimal probabilistic model for principal component analysis of manifold valued data (2019)
  20. 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)

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