Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Trends in Neural Architecture Search: Towards the Acceleration of Search
- Issue Date
- 2021
- Publisher
- IEEE
- Citation
- 12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION, v.2021-October, pp 421 - 424
- Pages
- 4
- Journal Title
- 12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION
- Volume
- 2021-October
- Start Page
- 421
- End Page
- 424
- ISSN
- 2162-1233
- Abstract
- In modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications. In this paper, the main research trends of neural architecture search (NAS) are classified as neuro-evolutionary algorithms, reinforcement learning based algorithms, and one-shot architecture search approaches. Furthermore, each research trend is introduced and finally all the major three trends are compared. Lastly, the future research directions of NAS research trends are discussed.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > Computer Engineering > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- Lee, Youn Kyu
- Engineering (Department of Computer Engineering)