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%Aigaion2 BibTeX export from LISA - Publications
%Wednesday 17 December 2014 05:56:59 AM

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%Aigaion2 BibTeX export from LISA - Publications
%Wednesday 17 December 2014 05:56:59 AM

@MISC{Bastien-Theano-2012,
        author = {Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Bergstra, James and Goodfellow, Ian J. and Bergeron, Arnaud and Bouchard, Nicolas and Bengio, Yoshua},
         title = {Theano: new features and speed improvements},
          year = {2012},
  howpublished = {Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop},
      abstract = {Theano is a linear algebra compiler that optimizes a userâ€™s symbolically-speciï¬ed
mathematical computations to produce efï¬cient low-level implementations. In
this paper, we present new features and efï¬ciency improvements to Theano, and
benchmarks demonstrating Theanoâ€™s performance relative to Torch7, a recently
introduced machine learning library, and to RNNLM, a C++ library targeted at
recurrent neural networks.}
}

@InProceedings{Bengio+Senecal-2003-small,
  author =       {Yoshua Bengio and Jean-S\'ebastien S\'en\'ecal},
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  title =        {Quick Training of Probabilistic Neural Nets by
                 Importance Sampling},
  year =         {2003},
}

@INPROCEEDINGS{bergstra+al:2010-scipy,
     author = {Bergstra, James and Breuleux, Olivier and Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Desjardins, Guillaume and Turian, Joseph and Warde-Farley, David and Bengio, Yoshua},
      month = jun,
      title = {Theano: a {CPU} and {GPU} Math Expression Compiler},
  booktitle = {Proceedings of the Python for Scientific Computing Conference ({SciPy})},
       year = {2010},
   location = {Austin, TX},
       note = {Oral Presentation},
   abstract = {Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPyâ€™s syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPyâ€™s syntax and semantics, while being statically typed and
functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates
them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learning algorithms implemented with Theano are from 1.6Ã— to 7.5Ã— faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the
CPU and between 6.5Ã— and 44Ã— faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.}
}

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