@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 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},
}

@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 user’s symbolically-specified
mathematical computations to produce efficient low-level implementations. In
this paper, we present new features and efficiency improvements to Theano, and
benchmarks demonstrating Theano’s performance relative to Torch7, a recently
introduced machine learning library, and to RNNLM, a C++ library targeted at
recurrent neural networks.}
}