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Found 5 papers, 0 papers with code
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness
Our proposed framework can push the fine-tuned model to approach the bias lower bound during downstream fine-tuning, indicating that the ineffectiveness of debiasing can be alleviated by overcoming the forgetting issue through regularizing successfully debiased attention heads based on the PLMs' bias levels from stages of pretraining and debiasing.
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.
