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 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 …

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?

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 …

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 …

Does it mean the regularization path is how to select the coordinate that could get faster convergence? I'm a little confused although I have heard about sparsity often. In addition, could you please give a …

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 …