Heterogeneous Student Knowledge Distillation From BERT Using a Lightweight Ensemble Framework-Reference-Cited by-同舟云学术

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Heterogeneous Student Knowledge Distillation From BERT Using a Lightweight Ensemble Framework

Published:2024 Issue: Volume:12 Page:33079-33088
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lin Ching-Sheng¹^ORCID,Tsai Chung-Nan²,Jwo Jung-Sing¹^ORCID,Lee Cheng-Hsiung¹^ORCID,Wang Xin³

Affiliation:

1. Master Program of Digital Innovation, Tunghai University, Taichung, Taiwan

2. Lam Research Japan GK, Yokohama, Kanagawa, Japan

3. Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA

Funder

National Science and Technology Council (NSTC) of Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10458136.pdf?arnumber=10458136

Reference59 articles.

1. A Golden Decade of Deep Learning: Computing Systems & Applications

2. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin;arXiv:1810.04805,2018

3. Language models are unsupervised multi-task learners;Radford;OpenAI Blog,2019

4. Language models are few-shot learners;Brown;arXiv:2005.14165,2020

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