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 16 articles )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Birk, Lothar; McCulloch, T. Luke: Robust generation of constrained B-spline curves based on automatic differentiation and fairness optimization (2018)
  2. Bart van Merrienboer, Alexander B. Wiltschko, Dan Moldovan: Tangent: Automatic Differentiation Using Source Code Transformation in Python (2017) arXiv
  3. Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo: TensorLayer: A Versatile Library for Efficient Deep Learning Development (2017) arXiv
  4. Jiacheng Zhang, Yanzhuo Ding, Shiqi Shen, Yong Cheng, Maosong Sun, Huanbo Luan, Yang Liu: THUMT: An Open Source Toolkit for Neural Machine Translation (2017) arXiv
  5. Jonas Rauber, Wieland Brendel, Matthias Bethge: Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models (2017) arXiv
  6. Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.: Deep learning in color: towards automated quark/gluon jet discrimination (2017)
  7. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic prolog for machine learning (2017)
  8. Trouillon, Théo; Dance, Christopher R.; Gaussier, Éric; Welbl, Johannes; Riedel, Sebastian; Bouchard, Guillaume: Knowledge graph completion via complex tensor factorization (2017)
  9. Alain, Guillaume; Bengio, Yoshua; Yao, Li; Yosinski, Jason; Thibodeau-Laufer, Éric; Zhang, Saizheng; Vincent, Pascal: GSNs: generative stochastic networks (2016)
  10. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  11. Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei: Edward: A library for probabilistic modeling, inference, and criticism (2016) arXiv
  12. Janaina Cruz Pereira, Ernesto Raul Caffarena, Cicero dos Santos: Boosting Docking-based Virtual Screening with Deep Learning (2016) arXiv
  13. Chen, Minmin; Weinberger, Kilian Q.; Xu, Zhixiang (Eddie); Sha, Fei: Marginalizing stacked linear denoising autoencoders (2015) ioport
  14. Bordes, Antoine; Glorot, Xavier; Weston, Jason; Bengio, Yoshua: A semantic matching energy function for learning with multi-relational data (2014)
  15. Mesnil, Grégoire; Bordes, Antoine; Weston, Jason; Chechik, Gal; Bengio, Yoshua: Learning semantic representations of objects and their parts (2014)
  16. Andersson, Joel; Åkesson, Johan; Diehl, Moritz: CasADi: A symbolic package for automatic differentiation and optimal control (2012)