I understand that neural networks can represent non-linear complex functions.
To clarify more,
My question is that a single neuron still computes F(X) = WX + b, which is a linear function.
Why not use a higher order function F(X) = WX^n + W1 X^(n-1) + ... +b.
I can imagine the increase in computational needed to implement this, but neural networks were considered to be time-consuming until we started using GPUs for parallel computations.
So if we ignore the implementation details to accomplish this for large networks, are there any inherent advantages to using higher-order neurons?
MLNoober OP t1_iqwyu29 wrote
Reply to [D] Why restrict to using a linear function to represent neurons? by MLNoober
Thank you for the replies.
I understand that neural networks can represent non-linear complex functions.
To clarify more,
My question is that a single neuron still computes F(X) = WX + b, which is a linear function.
Why not use a higher order function F(X) = WX^n + W1 X^(n-1) + ... +b.
I can imagine the increase in computational needed to implement this, but neural networks were considered to be time-consuming until we started using GPUs for parallel computations.
So if we ignore the implementation details to accomplish this for large networks, are there any inherent advantages to using higher-order neurons?