🔗 Who cites «Compressing Large-Scale Transformer-Based Models: A Case Study on BERT»

51 papers of our library cite it (of the 141,840 whose bibliographies we read) · 6 with a page here, the most cited first · ← back to «Compressing Large-Scale Transformer-Based Models: A Case Study on BERT»

1. Pre-trained Models for Natural Language Processing: A SurveyarXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.2020 · 1,753 citations
   Pretrained Transformers Improve Out-of-Distribution RobustnessAnnual Meeting of the Association for Computational Linguistics (ACL)Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.2020 · 443 citations
   Parameter-Efficient Transfer Learning with Diff PruningAnnual Meeting of the Association for Computational Linguistics (ACL)Demi Guo, Alexander Rush, Yoon Kim2021 · 357 citations
   Structured Pruning Learns Compact and Accurate ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mengzhou Xia, Zexuan Zhong, Danqi Chen2022 · 217 citations
   Few-Shot Self-Rationalization with Natural Language PromptsFindings of the Association for Computational LinguisticsAna Marasovic, Iz Beltagy, Doug Downey, et al.2022 · 134 citations
   BERT Busters: Outlier Dimensions that Disrupt TransformersFindings of the Association for Computational LinguisticsOlga Kovaleva, Saurabh Kulshreshtha, Anna Rogers, et al.2021 · 90 citations

45 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/ganesh-2021-compressing-large-scale-transformer-based/cited-by/