Handwritten Digit Recognition with a Committee of Deep Neural Nets on   GPUs
  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. 
Dan C. Cire\c{s}an, Ueli Meier, Luca M. Gambardella and J\"urgen   Schmidhuber
cs.LG cs.AI cs.CV cs.NE