Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [l1-regularization]

For questions related to $L_1$ regularization, also known as LASSO regularization.

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When is using weight regularization bad?

Regularization of weights (e.g. L1 or L2) keeps them small and standardized, which can help reduce data overfitting. From this article, regularization sounds favorable in many cases, but is it always encouraged? Are there scenarios in which it…
mark mark
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Why does L1 regularization yield sparse features?

In contrast to L2 regularization, L1 regularization usually yields sparse feature vectors and most feature weights are zero. What's the reason for the above statement - could someone explain it mathematically, and/or provide some intuition (maybe…
stoic-santiago
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Which is a better form of regularization: lasso (L1) or ridge (L2)?

Given a ridge and a lasso regularizer, which one should be chosen for better performance? An intuitive graphical explanation (intersection of the elliptical contours of the loss function with the region of constraints) would be helpful.
jaeger6
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Would either $L_1$ or $L_2$ regularisation lower the MSE on the training and test data?

Consider linear regression. The mean squared error (MSE) is 120.5 for the training dataset. We've reached the minimum for the training data. Is it possible that by applying Lasso (L1 regularization) we would get a lower MSE for the training data?…
user6394019
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Does L1/L2 Regularization help reach an optimum result faster?

I understand that L1 and L2 regularization helps to prevent overfitting. My question is then, does that mean they also help a neural network learn faster as a result? The way I'm thinking is that since the regularization techniques reduce weights…
Mark
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Since ReLU activations also result in a sparse network, does it have the same "feature selection" property as L1 regularization?

From Deep Learning (Courville, Goodfellow, Bengio), a ReLU activation often "dies" because One drawback to rectified linear units is that they cannot learn via gradient based methods on examples for which their activation is zero. Similarly, L1…
rac.coon
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How to prove that a regularisation method simplified a neural network?

There are a few ways to regularise a neural network, for example dropout or the L1. Now, both these methods, and possibly most other regularisation methods, tend to remove from, or simplify the neural network. The Dropout deactivates nodes and the…
Marcus
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What is the effect of too harsh regularization?

While training a CNN model, I used an l1_l2 regularization (i.e. I applied both $L_1$ and $L_2$ regularization) on the final layers. While training, I saw the training and validation losses are dropping very nicely, but the accuracies aren't…
Sepehr Golestanian
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