Torch is a machine learning library written in C++ that works on most Unix/Linux platforms. It can be used to train MLPs, RBFs, HMMs, Gaussian Mixtures, Kmeans, Mixtures of experts, Parzen Windows, KNN, and can be easily extended so that you can add your own machine learning algorithms. (Source:

References in zbMATH (referenced in 20 articles )

Showing results 1 to 20 of 20.
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  1. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  2. Han Wang, Linfeng Zhang, Jiequn Han, Weinan E: DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics (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. Richard Wei, Vikram Adve, Lane Schwartz: DLVM: A modern compiler infrastructure for deep learning systems (2017) arXiv
  5. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  6. Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi: DeepChrome: Deep-learning for predicting gene expression from histone modifications (2016) arXiv
  7. Žbontar, Jure; Lecun, Yann: Stereo matching by training a convolutional neural network to compare image patches (2016)
  8. Doermann, David (ed.); Tombre, Karl (ed.): Handbook of document image processing and recognition (2014)
  9. Mesnil, Grégoire; Bordes, Antoine; Weston, Jason; Chechik, Gal; Bengio, Yoshua: Learning semantic representations of objects and their parts (2014)
  10. Hazrati Fard, Seyed Mehdi; Hamzeh, Ali; Hashemi, Sattar: Using reinforcement learning to find an optimal set of features (2013)
  11. Kovacs, Tim; Egginton, Robert: On the analysis and design of software for reinforcement learning, with a survey of existing systems (2011) ioport
  12. Jin, Xiao-Bo; Liu, Cheng-Lin; Hou, Xinwen: Regularized margin-based conditional log-likelihood loss for prototype learning (2010)
  13. Wu, Kuo-Ping; Wang, Sheng-De: Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space (2009)
  14. Gaudel, Romaric; Sebag, Michèle; Cornuéjols, Antoine: A phase transition-based perspective on multiple instance kernels (2008)
  15. Schlapbach, Andreas; Liwicki, Marcus; Bunke, Horst: A writer identification system for on-line whiteboard data (2008)
  16. Mariéthoz, Johnny; Bengio, Samy: A kernel trick for sequences applied to text-independent speaker verification systems (2007)
  17. Alexandros Karatzoglou, David Meyer, Kurt Hornik: Support Vector Machines in R (2006)
  18. Demirdjian, David; Ko, Teresa; Darrell, Trevor: Nudge nudge wink wink: elements of face-to-face conversation for embodied conversational agents (2005) ioport
  19. Langlais, Philippe; Gandrabur, Simona; Leplus, Thomas; Lapalme, Guy: The long-term forecast for weather bulletin translation (2005) ioport
  20. Cardinaux, Fabien; Sanderson, Conrad; Marcel, Sébastien: Comparison of MLP and GMM classifiers for face verification on XM2VTS (2003)