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 95 articles )

Showing results 1 to 20 of 95.
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  1. Jiazhen Gu, Xuchuan Luo, Yangfan Zhou, Xin Wang: Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing (2022) arXiv
  2. Amini Niaki, Sina; Haghighat, Ehsan; Campbell, Trevor; Poursartip, Anoush; Vaziri, Reza: Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (2021)
  3. Cruttwell, Geoffrey; Gallagher, Jonathan; Pronk, Dorette: Categorical semantics of a simple differential programming language (2021)
  4. Frank Schäfer, Mohamed Tarek, Lyndon White, Chris Rackauckas: AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia (2021) arXiv
  5. Haghighat, Ehsan; Bekar, Ali Can; Madenci, Erdogan; Juanes, Ruben: A nonlocal physics-informed deep learning framework using the peridynamic differential operator (2021)
  6. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  7. Haghighat, Ehsan; Raissi, Maziar; Moure, Adrian; Gomez, Hector; Juanes, Ruben: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (2021)
  8. Holbrook, Andrew J.; Lemey, Philippe; Baele, Guy; Dellicour, Simon; Brockmann, Dirk; Rambaut, Andrew; Suchard, Marc A.: Massive parallelization boosts big Bayesian multidimensional scaling (2021)
  9. Le, Linh; Xie, Ying; Raghavan, Vijay V.: KNN loss and deep KNN (2021)
  10. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  11. Lykkegaard, Mikkel B.; Dodwell, Tim J.; Moxey, David: Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy (2021)
  12. Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson: Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem (2021) not zbMATH
  13. Qin, Shanshan; Mudur, Nayantara; Pehlevan, Cengiz: Contrastive similarity matching for supervised learning (2021)
  14. Schoenholz, Samuel S.; Cubuk, Ekin D.: JAX, M.D. a framework for differentiable physics (2021)
  15. Zhu, Qiming; Liu, Zeliang; Yan, Jinhui: Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (2021)
  16. 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
  17. Bloem-Reddy, Benjamin; Teh, Yee Whye: Probabilistic symmetries and invariant neural networks (2020)
  18. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  19. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  20. Guo, Jian; He, He; He, Tong; Lausen, Leonard; Li, Mu; Lin, Haibin; Shi, Xingjian; Wang, Chenguang; Xie, Junyuan; Zha, Sheng; Zhang, Aston; Zhang, Hang; Zhang, Zhi; Zhang, Zhongyue; Zheng, Shuai; Zhu, Yi: GluonCV and GluonNLP: deep learning in computer vision and natural language processing (2020)

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