Papers with transformer-specific dropout methods

1 papers
LoRA Meets Dropout under a Unified Framework (2024.findings-acl)

Copied to clipboard

Challenge: Parameter-efficientfinetuning (PEFT) has gained popularity as a lightweight approach for model customization.
Approach: They propose a parameter-efficient dropout method that is overfitting-prone and parameter-freezed.
Outcome: The proposed method is superior to existing methods and compares with transformer-specific methods.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations