[billderose]

Hinton's DropOut [1] and Wan's DropConnect [2] have ameliorated some of the overfitting issues present in traditional NN's. In fact, DropConnect in conjunction with deep learning are responsible for new records being set on classical datasets such as MNIST.

[1] http://arxiv.org/pdf/1207.0580.pdf [2] http://cs.nyu.edu/~wanli/dropc/

[agibsonccc]

Dropout is actually a knob on any neural network. These are used in image recognition as well as text and other areas.

The fuzzing creates a very similar effect to convolutional nets where it can learn different poses of an image.

[im3w1l]

It's pretty funny, I saw DropConnect described in a stackoverflow answer that predated the paper you reference. It was an incorrect answer on how to do dropout. I shall try to find it tomorrow.