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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)
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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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