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Found 3 papers, 0 papers with code
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
We then demonstrate the theoretical mechanism of our LoRA Dropout mechanism from the perspective of sparsity regularization by providing a generalization error bound under this framework.
Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
With the increasingly powerful performances and enormous scales of Pretrained Language Models (PLMs), promoting parameter efficiency in fine-tuning has become a crucial need for effective and efficient adaptation to various downstream tasks.
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling
In this way, these data can be also very informative if we can effectively exploit these complementary labels, i. e., the classes that a sample does not belong to.
