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GPT-NAS: Neural Architecture Search Meets Generative Pre-Trained Transformer Model [PDF]

Big Data Mining and Analytics
The pursuit of optimal neural network architectures is foundational to the progression of Neural Architecture Search (NAS). However, the existing NAS methods suffer from the following problem using traditional search strategies, i.e., when facing a large
Caiyang Yu, Xianggen Liu, Yifan Wang, Yun Liu, Wentao Feng, Xiong Deng, Chenwei Tang, Jiancheng Lv   +7 more
doaj   +4 more sources

NAS-Unet: Neural Architecture Search for Medical Image Segmentation [PDF]

IEEE Access, 2019
Neural architecture search (NAS) has significant progress in improving the accuracy of image classification. Recently, some works attempt to extend NAS to image segmentation which shows preliminary feasibility.
Yu Weng, Tianbao Zhou, Yujie Li, Xiaoyu Qiu   +3 more
doaj   +4 more sources

D2NAS: Efficient Neural Architecture Search With Performance Improvement and Model Size Reduction for Diverse Tasks

IEEE Access
Neural Architecture Search (NAS) has proven valuable in many applications such as computer vision. However, its true potential is unveiled when applied to less-explored domains.
Jungeun Lee, Seungyub Han, Jungwoo Lee
doaj   +3 more sources

Neural architecture search applying optimal stopping theory [PDF]

Frontiers in Artificial Intelligence
Neural architecture search (NAS) exploration requires tremendous amounts of computational power to properly explore. This makes exploration of modern NAS search spaces impractical for researchers due to the infrastructure investments required and the ...
Matthew Sheehan, Oleg Yakimenko
doaj   +2 more sources

Unveil Fundamental Graph Properties for Neural Architecture Search [PDF]

Advanced Science
Deep learning profoundly impacts various areas, such as face recognition and language translation. Owing to the increasingly high computational costs of training neural architectures, it is intractable to manually examine the performance of various ...
Zhenhan Huang, Tejaswini Pedapati, Pin‐Yu Chen, Chunheng Jiang, Jianxi Gao   +4 more
doaj   +2 more sources

Neural architecture search using network embedding and generative adversarial networks [PDF]

Scientific Reports
Surrogate models are used by recently proposed algorithms as a means of forecasting neural architecture performance. Rather than training the network from scratch, which speeds up evaluation of performance in the search for neural architecture.
Morteza Yousefi, Vahid Mehrdad, Mohammad Bagher Dowlatshahi   +2 more
doaj   +2 more sources

Population-based guiding for evolutionary neural architecture search [PDF]

Scientific Reports
Neural Architecture Search (NAS)—combined with biology-inspired evolutionary methods—can help discover suitable architectures tailored to a given objective.
Stefan Dendorfer, Andreas M. Kist
doaj   +2 more sources

Gradient Descent Effects on Differential Neural Architecture Search: A Survey

IEEE Access, 2021
Gradient Descent, an effective way to search for the local minimum of a function, can minimize training and validation loss of neural architectures and also be incited in an appropriate order to decrease the searching cost of neural architecture search ...
Santanu Santra, Jun-Wei Hsieh, Chi-Fang Lin   +2 more
doaj   +1 more source

Differentiable Architecture Search Based on Coordinate Descent

IEEE Access, 2021
Neural architecture search (NAS) is an automated method searching for the optimal network architecture by optimizing the combinations of edges and operations.
Pyunghwan Ahn, Hyeong Gwon Hong, Junmo Kim   +2 more
doaj   +1 more source

Topology-Sensitive Neural Architecture Search for Language Modeling

IEEE Access, 2021
Recently Neural Architecture Search has drawn interest from researchers because of its ability to learn neural network architectures from data automatically.
Quan Du, Nuo Xu, Yinqiao Li, Tong Xiao, Jingbo Zhu   +4 more
doaj   +1 more source