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 simple/insensitive model to …

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 machine learning …

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?

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 such …

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 linear …

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 regularization term …

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 regularization are the same for SGD …

Aug 1, 2016 · Regularization helps in keeping these coefficients at lower values, hence, the curve is smooth. We now have less training points on the curve, more training error, but less test error, …

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 case it 'works' …

Sep 28, 2017 · The effect of applying the L2-norm regularization in neural networks is that it penalizes large weights in the model. How does this prevent overfitting? My assumption is that large weights …