Controlled Randomness Improves the Performance of Transformer Models-Reference-Cited by-同舟云学术

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Controlled Randomness Improves the Performance of Transformer Models

Published:2023-12-15 Issue: Volume:21 Page:1805-1810
ISSN:
Container-title:2023 International Conference on Machine Learning and Applications (ICMLA)
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Author:

Deußer Tobias¹^ORCID,Zhao Cong¹,Krämer Wolfgang²,Leonhard David¹,Bauckhage Christian¹,Sifa Rafet¹

Affiliation:

1. University of Bonn,Bonn,Germany

2. Deutsche Telekom,Bonn,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10459339/10459727/10460040.pdf?arnumber=10460040

Reference73 articles.

1. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin

2. ELECTRA: Pre-training text encoders as discriminators rather than generators;Clark

3. Exploring the limits of transfer learning with a unified text-to-text transformer;Raffel;The Journal of Machine Learning Research,2020

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

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