A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Simple way to make recurrent networks remember longer with ReLU

Researchers found a quiet trick that makes some neural nets hold on to memories much better.
Instead of starting with random numbers, they initialize the loop of connections with the identity — think of it like a clear path for signals to travel.
Paired with simple ReLU units, this lets information flow without fading away or exploding, so the model can learn patterns that happen far apart in time.
The idea is actually very simple, and yet it helps these nets match the performance of bigger, fancier designs on several tests.
You can picture it as opening a straight road through a maze so important signals dont get lost.
It works on toy tasks, on language work and even on speech tests, and often trains faster, uses less fuss.
Not magic, just a small change at the start that makes a big difference later, and the trick could be useful for anyone building systems that need real long-term memory.
Try it, it might surprise you.

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A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

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