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Found 4 papers, 3 papers with code
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings.
Ranked #1 on
Subjectivity Analysis
on SUBJ
Robust Regularization with Adversarial Labelling of Perturbed Samples
The ALPS regularization objective is formulated as a min-max problem, in which the outer problem is minimizing an upper-bound of the VRM loss, and the inner problem is L$_1$-ball constrained adversarial labelling on perturbed sample.
Regularizing Neural Networks via Adversarial Model Perturbation
This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better.
Ranked #8 on
Image Classification
on SVHN
