ML//neural network//weight sharing

Reusing the same parameters at many positions instead of learning separate ones for each. The reason an RNN or LSTM needs one small set of weights for a sequence of any length.

Reusing the same parameters at many positions instead of learning separate ones for each. The reason an RNN or LSTM needs one small set of weights for a sequence of any length.

Bakes in an assumption: the rule is the same at every position (every timestep, every image patch). Forces a general rule instead of position-by-position memorization.

Also the core trick of CNNs, where one filter slides across the whole image.

Lets a single trained model handle inputs of any length or size without changing shape.