Self-attention Mechanisms in Transformer Architectures
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Keywords

Self-attention
Transformer architectures
Long-range dependencies

How to Cite

[1]
Amir Khan, “Self-attention Mechanisms in Transformer Architectures: Studying self-attention mechanisms in transformer architectures and their role in capturing long-range dependencies in sequential data”, Journal of AI in Healthcare and Medicine, vol. 1, no. 1, pp. 11–21, May 2021, Accessed: Nov. 13, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/24

Abstract

Self-attention mechanisms in transformer architectures have revolutionized natural language processing and sequential data modeling. This paper provides a comprehensive overview of self-attention mechanisms, detailing their key components and operations. We discuss how self-attention enables transformers to capture long-range dependencies, improving performance in various tasks. Furthermore, we explore recent advancements and extensions of self-attention, such as multi-head attention and scaled dot-product attention. Finally, we discuss challenges and future directions in the field of self-attention research.

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References

Tatineni, Sumanth. "Federated Learning for Privacy-Preserving Data Analysis: Applications and Challenges." International Journal of Computer Engineering and Technology 9.6 (2018).

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