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 increase complexity/sensitivity.
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 transformation they infer to relate input and output data.
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 to the c...
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 same thing? or are these two completely separate practices sharing the same names? (if relevant) what are the similarities and differences between the two?
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 regularization reduce
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?
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 but they are different for Adam.
Since the L2-regularization squares the weights, L2(w) will change much more for the same change of weights when we have higher weights. This is why the function is convex when you plot it. For L1 however, the change of L1(w) per change of weights are the same regardless of what your weights are - this leads to a linear function.
Terms like "regularization of sequences" have been around in mathematics for a long time (certainly since the 1920s), which has a meaning fairly closely related to the regularization of ill-posed problems. I suspect the use of the word in mathematics would derive from its use in engineering ("regularization of flow" for example).
Binary cross-entropy is commonly used for binary classification problems. The effect of regularization in this context may include: L1 Regularization: It can still induce sparsity in the weight vectors, promoting some weights to become exactly zero. This can be useful for feature selection even in the context of binary cross-entropy loss.
