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@INCOLLECTION{Collobert_SPRINGER_2012,
         author = {Collobert, Ronan and Kavukcuoglu, Koray and Farabet, Cl{\'{e}}ment},
         editor = {Montavon, Gr{\'{e}}goire and Orr, Genevi{\`{e}}ve and M{\"{u}}ller, K. -R.},
       keywords = {machine-learning software},
       projects = {Idiap},
          title = {Implementing Neural Networks Efficiently},
      booktitle = {Neural Networks: Tricks of the Trade},
        edition = {Second},
           year = {2012},
      publisher = {Springer},
       abstract = {Neural networks and machine learning algorithms in general require a flexible environment where new algorithm prototypes and experiments can be set up as quickly as possible with best possible computational performance. To that end, we provide a new framework called Torch7, that is especially suited to achieve both of these competing goals. Torch7 is a versatile numeric computing framework and machine learning library that extends a very lightweight and powerful programming language Lua. Its goal is to provide a flexible environment to design, train and deploy learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High performance is obtained via efficient OpenMP/SSE and CUDA implementations of low-level numeric routines. Torch7 can also easily be interfaced to third-party software thanks to Lua’s light C interface.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Collobert_SPRINGER_2012.pdf}
}