GELU: A smarter neural switch that helps models learn faster
Meet GELU, a new way for neural nets to decide when to pass a signal.
Instead of a hard on or off it uses a random choice that depends on the value itself, so big signals more likely get through, small ones less likely.
That means it often keeps signal that matters while quietly dropping what does not.
It feels like a gentle, value-aware coin flip, and it can make training smoother, the model learn quicker, and generalize better in tests.
People tried it against common switches and saw better results across tasks, not just once but repeatedly.
The idea mixes the safety of sometimes turning things off with the care of respecting each neuron's number, so networks don't lose subtle clues.
It is simple to add and often gives a boost, though not magic, and it works well in lots of places.
Try it if you want models that are a bit smarter about what to keep and what to ignore, they may surprise you.
Read article comprehensive review in Paperium.net:
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error LinearUnits
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