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Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs

Dan C. Ciresan, Ueli Meier, Luca M. Gambardella, Jurgen Schmidhuber
IDSIA / USI-SUPSI, Dalle Molle Institute for Artificial Intelligence, Galleria 2, 6928 Manno, Switzerland
arXiv:1103.4487v1 [cs.LG] (23 Mar 2011)

@article{2011arXiv1103.4487C,

   author={Cire{c s}an}, D.~C. and {Meier}, U. and {Gambardella}, L.~M. and {Schmidhuber}, J.},

   title={"{Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1103.4487},

   primaryClass={"cs.LG"},

   keywords={Computer Science – Learning, Computer Science – Artificial Intelligence, Computer Science – Computer Vision and Pattern Recognition, Computer Science – Neural and Evolutionary Computing},

   year={2011},

   month={mar},

   adsurl={http://adsabs.harvard.edu/abs/2011arXiv1103.4487C},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent substantial improvement by others dates back 7 years (error rate 0.4%) . Recently we were able to significantly improve this result, using graphics cards to greatly speed up training of simple but deep MLPs, which achieved 0.35%, outperforming all the previous more complex methods. Here we report another substantial improvement: 0.31% obtained using a committee of MLPs.
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