AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning

12 October 2022

Papers citing "AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning"

8 / 8 papers shown

Title
The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach David Bertoin Eduardo Hugo Sanchez Mehdi Zouitine Emmanuel Rachelson 23 0 0 25 Sep 2024
Unifying Graph Contrastive Learning via Graph Message Augmentation Ziyan Zhang Bo Jiang Jin Tang Bin Luo 17 1 0 08 Jan 2024
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning Runxin Xu Fuli Luo Zhiyuan Zhang Chuanqi Tan Baobao Chang Songfang Huang Fei Huang LRM 136 178 0 13 Sep 2021
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks Hieu H. Pham Quoc V. Le 58 56 0 05 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 137 656 0 28 Dec 2020
Big Bird: Transformers for Longer Sequences Manzil Zaheer Guru Guruganesh Kumar Avinava Dubey Joshua Ainslie Chris Alberti ... Philip Pham Anirudh Ravula Qifan Wang Li Yang Amr Ahmed VLM 251 2,012 0 28 Jul 2020
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models Cheolhyoung Lee Kyunghyun Cho Wanmo Kang MoE 235 205 0 25 Sep 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,943 0 20 Apr 2018