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 …

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 …

The learning rate parameter ($\\nu \\in [0,1]$) in Gradient Boosting shrinks the contribution of each new base model -typically a shallow tree- that is added in the series. It was shown to dramatically

Jul 24, 2017 · In what circumstances should one consider using regularization methods (ridge, lasso or least angles regression) instead of OLS? In case this helps steer the discussion, my main interest is …

Apr 30, 2020 · L2 regularization (or weight decay) is different from reconstruction as it is used to control network weights. Of course you can try L2 regularization if you think that your network is under/over …