TENAS: Using Taylor Expansion and Channel-Level Skip Connection for Neural Architecture Search
There is growing interest in automating designing good neural network architectures. The neural architecture search (NAS) methods proposed recently have significantly reduced the architecture search cost by sharing parameters, but there is still a challenging problem in designing search space. The e...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9845403/ |