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 81 articles )

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  1. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  2. 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)
  3. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  4. Qin, Shanshan; Mudur, Nayantara; Pehlevan, Cengiz: Contrastive similarity matching for supervised learning (2021)
  5. 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)
  6. 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
  7. Bloem-Reddy, Benjamin; Teh, Yee Whye: Probabilistic symmetries and invariant neural networks (2020)
  8. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  9. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  10. 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)
  11. Hottung, André; Tanaka, Shunji; Tierney, Kevin: Deep learning assisted heuristic tree search for the container pre-marshalling problem (2020)
  12. Hughes, Mark C.: A neural network approach to predicting and computing knot invariants (2020)
  13. Joshua G. Albert: JAXNS: a high-performance nested sampling package based on JAX (2020) arXiv
  14. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  15. Laue, Sören; Mitterreiter, Matthias; Giesen, Joachim: A simple and efficient tensor calculus for machine learning (2020)
  16. Miolane, Nina; Guigui, Nicolas; Le Brigant, Alice; Mathe, Johan; Hou, Benjamin; Thanwerdas, Yann; Heyder, Stefan; Peltre, Olivier; Koep, Niklas; Zaatiti, Hadi; Hajri, Hatem; Cabanes, Yann; Gerald, Thomas; Chauchat, Paul; Shewmake, Christian; Brooks, Daniel; Kainz, Bernhard; Donnat, Claire; Holmes, Susan; Pennec, Xavier: Geomstats: a Python package for Riemannian geometry in machine learning (2020)
  17. Reizenstein, Jeremy F.; Graham, Benjamin: Algorithm 1004: The iisignature library: efficient calculation of iterated-integral signatures and log signatures (2020)
  18. René, Alexandre; Longtin, André; Macke, Jakob H.: Inference of a mesoscopic population model from population spike trains (2020)
  19. Škrlj, Blaž; Kralj, Jan; Lavrač, Nada: Embedding-based silhouette community detection (2020)
  20. Sun, Luning; Gao, Han; Pan, Shaowu; Wang, Jian-Xun: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2020)

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