A few months ago we had a small post here discussing different weight initializations, and I remember +Sander Dieleman and a few others had a good discussion . It is fairly important to do good weight initializations, as the rewards are non-trivial.
For example, AlexNet, which is fairly popular, from Alex's One Weird Trick paper, converges in 90 epochs (using alex's 0.01 stdv initialization).
I retrained it from scratch using the weight initialization from Yann's 98 paper, and it converges to the same error within just 50 epochs, so technically +Alex Krizhevsky could've rewritten the paper with even more stellar results (training Alexnet in 8 hours with 8 GPUs).
In fact, more interestingly, just by doing good weight initialization, I even removed the Local Response Normalization layers in AlexNet with no drop in error.
I've noticed the same trend with several other imagenet-size models, like Overfeat and OxfordNet, they converge in much lesser epochs than what is reported in the paper, just by doing this small change in weight initialization.
If you want the exact formulae, look at the two links below:
And read yann's 98 paper Efficient Backprop: http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf

On that note, Surya Ganguli's talk this year at NIPS workshop wrt optimal weight initializations triggered this post. Check out his papers on that side, great work.