You're correct, and the term you're looking for is "regularisation".
There are two common ways of doing this:
* L1 or L2 regularisation: penalises models whose weight matrices are complex (in the sense of having lots of large elements)
* Dropout: train on random subsets of the neurons to force the model to rely on simple representations that are distributed robustly across its weights
There are two common ways of doing this: * L1 or L2 regularisation: penalises models whose weight matrices are complex (in the sense of having lots of large elements) * Dropout: train on random subsets of the neurons to force the model to rely on simple representations that are distributed robustly across its weights