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Unlocking Tuning-free Generalization: Minimizing the PAC-Bayes Bound with Trainable Priors
It is widely recognized that the generalization ability of neural networks can be greatly enhanced through carefully designing the training procedure.
Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent
It is well known that the finite step-size ($h$) in Gradient Descent (GD) implicitly regularizes solutions to flatter minima.
Learning Sparsity-Promoting Regularizers using Bilevel Optimization
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images.
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)
We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems.
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