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