Run fully connected artificial neural networks with dropout applied (mini)batchwise, rather than samplewise. Given two hidden layers each subject to 50% dropout, the corresponding matrix multiplications for forward- and back-propagation is 75% less work as the dropped out units are not calculated.
☆15May 18, 2015Updated 10 years ago
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