Wordreg: Mitigating the Gap between Training and Inference with Worst-Case Drop Regularization-Reference-Cited by-同舟云学术

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Wordreg: Mitigating the Gap between Training and Inference with Worst-Case Drop Regularization

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
language:
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Author:

Xia Jun¹,Wang Ge²,Hu Bozhen²,Tan Cheng²,Zheng Jiangbin²,Xu Yongjie²,Li Stan Z.²

Affiliation:

1. Zhejiang University,Hangzhou,China,310058

2. Westlake University,School of Engineering,Hangzhou,China,310030

Funder

National Natural Science Foundation of China

Westlake University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095552.pdf?arnumber=10095552

Reference25 articles.

1. Xlnet: Generalized autoregressive pretraining for language understanding;yang;NeurIPS,2019

2. Bert: Pretraining of deep bidirectional transformers for language understanding;devlin;NAACL,2019

3. Roberta: A robustly optimized bert pretraining approach;liu,2019

4. ELECTRA: Pre-training text encoders as discriminators rather than generators;clark;ICLRE,2020

5. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy;International Conference on Learning Representations,2021

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