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Neural networks that learn non-linearity without activation functions [pdf]

https://www.tahabouhsine.com/nmn/assets/deep_learning_two_point_o_point_one.pdf
18•mlnomadpy•3d ago

Comments

mlnomadpy•3d ago
hello everyone, am taha,

I was able to create a new kernel that allows you to learn non-linearity without using activation functions, making the models whitebox, and without any information loss.

MiniGPT with huggingface datasets streaming: https://www.kaggle.com/code/skywolfmo/yat-nnx-minigpt-finewe...

rytill•3h ago
Why would one have motivation to not use activation functions?

To my knowledge they’re a negligible portion of the total compute during training or inference and work well to provide non-linearity.

Very open to learning more.

russfink•3h ago
One reason might be expressing the constructs in a different domain, eg homomorphic encrypted evaluators.
nikolayasdf123•4h ago
I misread this as if "there is no non-linearity". there is still non-linearity, it is just renamed and reshuffled into new operators. basically renaming apples into oranges.
imtringued•17m ago
Well, it's more like fruits and vegetables. The author proposed a normalized inner product as replacement for the standard inner product.

It's not an activation function, because it has the learnable weights of a linear projection (mat vec multiplication) and the clamping properties of an activation function all in one.

My personal issue with the proposal is that it essentially doubles the amount of memory needed on-chip.

Yat-Product GEMMV now needs to store the running total of the inner product and the norm of the input vectors. That's a big cost increase for something that might not improve performance all that much.

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