Is regularization really ever used to reduce underfitting? In my experience, regularization is applied on a complex/sensitive model to reduce complexity/sensitvity, but never on a …

Mar 19, 2023 · Regularization is a way of sacrificing the training loss value in order to improve some other facet of performance, a major example being to sacrifice the in-sample fit of a …

The regularization can also be interpreted as prior in a maximum a posteriori estimation method. Under this interpretation, the ridge and the lasso make different assumptions on the class of …

Is regularization a way to ensure regularity? i.e. capturing regularities? Why do ensembling methods like dropout, normalization methods all claim to be doing regularization?

Apr 6, 2025 · I'm reading [Ilya Loshchilov's work] [1] on decoupled weight decay and regularization. The big takeaway seems to be that weight decay and $L^2$ norm …

Dec 3, 2017 · When implementing a neural net (or other learning algorithm) often we want to regularize our parameters $\\theta_i$ via L2 regularization. We do this usually by adding a …

By definition, a regularization parameter is any term that is in the optimized loss, but not the problem loss. Since the learning rate is acting like an extra quadratic term in the optimized …

Mar 13, 2015 · A common way to reduce overfitting in a machine learning algorithm is to use a regularization term that penalizes large weights (L2) or non-sparse weights (L1) etc. How can …

Aug 1, 2016 · Compare, for example, a second-order polynomial without regularization to a fourth-order polynomial with it. The latter can posit big coefficients for the third and fourth powers so …

Why does regularization work You can solve it with regularization, but you should have some good ways to know/estimate by what extent you wish to regularize. In the high-dimensional …