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Cited by 689

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

An Attentive Survey of Attention Models

Open access
Chaudhari, Sneha, Mithal, Varun, Polatkan, Gungor, Ramanath, Rohan·2021·ACM Transactions on Intelligent Systems and Technology
DOI 10.1145/3465055
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Abstract

Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy that groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.

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Metadata

Authors
Chaudhari, S., Mithal, V., Polatkan, G., & Ramanath, R.
Venue
ACM Transactions on Intelligent Systems and Technology
Publisher
Association for Computing Machinery (ACM)
Published
2021-10-22
Volume
12
Issue
5
Pages
1-32
ISSN
  • 2157-6904 (ISSN-L)
  • 2157-6912
DOI
10.1145/3465055
OpenAlex
W2941531368
Cited by
689
References
110
FWCI
42.59
Type
journal article
Language
en
Open access
green
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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Chaudhari, S., Mithal, V., Polatkan, G., & Ramanath, R. (2021). An Attentive Survey of Attention Models. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3465055

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