bibliography.bib

%Aigaion2 BibTeX export from LISA - Publications
%Saturday 16 May 2015 09:18:54 PM

@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 = {NIPS Workshop: Deep Learning and Unsupervised Feature Learning}
}


%Aigaion2 BibTeX export from LISA - Publications
%Saturday 16 May 2015 09:19:06 PM

@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},
}

@INPROCEEDINGS{bahdanau2015neural,
  title={Neural machine translation by jointly learning to align and translate},
  author={Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
  booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2015}
}

@article{gregor2015draw,
  title={DRAW: A recurrent neural network for image generation},
  author={Gregor, Karol and Danihelka, Ivo and Graves, Alex and Wierstra, Daan},
  journal={arXiv preprint arXiv:1502.04623},
  year={2015}
}


%Aigaion2 BibTeX export from LISA - Publications
%Monday 18 May 2015 01:22:32 PM

@INPROCEEDINGS{Goodfellow-et-al-ICML2013,
    author = {Goodfellow, Ian J. and Warde-Farley, David and Mirza, Mehdi and Courville, Aaron and Bengio, Yoshua},
    editor = {Dasgupta, Sanjoy and McAllester, David},
     title = {Maxout Networks},
      year = {2013},
     pages = {1319-1327},
  crossref = {ICML13},
  abstract = {We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We dene a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropouts fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classication performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.}
}

@INPROCEEDINGS{Pascanu-et-al-ICML2013,
    author = {Pascanu, Razvan and Mikolov, Tomas and Bengio, Yoshua},
     title = {On the difficulty of training Recurrent Neural Networks},
      year = {2013},
  crossref = {ICML13},
  abstract = {There are two widely known issues with properly training recurrent neural networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section.}
}

@ARTICLE{pylearn2_arxiv_2013,
    author = {Goodfellow, Ian J. and Warde-Farley, David and Lamblin, Pascal and Dumoulin, Vincent and Mirza, Mehdi and Pascanu, Razvan and Bergstra, James and Bastien, Fr{\'{e}}d{\'{e}}ric and Bengio, Yoshua},
     title = {Pylearn2: a machine learning research library},
   journal = {arXiv preprint arXiv:1308.4214},
      year = {2013},
       url = {http://arxiv.org/abs/1308.4214},
  abstract = {Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially.}
}

@unpublished{Bengio-et-al-2015-Book,
    title={Deep Learning},
    author={Yoshua Bengio and Ian J. Goodfellow and Aaron Courville},
    note={Book in preparation for MIT Press},
    url={http://www.iro.umontreal.ca/~bengioy/dlbook},
    year={2015}
}

@ONLINE{hdf5,
    author = {{The HDF Group}},
    title = "{Hierarchical Data Format, version 5}",
    year = {1997-2015},
    note = {http://www.hdfgroup.org/HDF5/}
}