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In the years leading up to this point sparsity was shown to be beneficial for deep learning, both because it represents information in a more robust manner and because it leads to significant computational efficiency (if most of your neurons are outputting zero, you can in effect ignore most of them and compute things much faster).
Incidentally, researchers in computational neuroscience first introduced the importance of sparse computation in the context of the brain's visual system, a decade before it was explored in the context of machine learning. The simplicity of the function, and its derivatives, makes it much faster to work with than the exponential sigmoid or the trigonometric tanh.
As with the use of GPUs, this turns out to not just be a small boost but really important for being able to scale neural nets to the point where they perform well on challenging problems.
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