irregularizer

the algorithm used an irregularizer to prevent overfitting.

we employed an l1 irregularizer to promote sparsity in the model.

the regularization strength of the irregularizer was carefully tuned.

an adaptive irregularizer can improve generalization performance.

the irregularizer penalized large weights in the neural network.

we compared the performance with and without the irregularizer.

the choice of irregularizer significantly impacts the model's complexity.

a total variation irregularizer is useful for image denoising.

the irregularizer helped to reduce the model's variance.

we experimented with different types of irregularizers for this task.

the irregularizer’s coefficient was optimized using gradient descent.