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 …

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 …

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 …

Mar 19, 2023 · Regularization - penalty for the cost function, L1 as Lasso & L2 as Ridge Cost/Loss Function - L1 as MAE (Mean Absolute Error) and L2 as MSE (Mean Square Error) Are [1] and [2] the …

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 …

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 …

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 loss, but has …

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 long as this …

May 26, 2022 · L1 Regularization (Lasso): This term adds the absolute values of the weights to the loss function. It tends to induce sparsity in the weight vectors, meaning that some weights become …