Accelerating multi-objective neural architecture search by random-weight evaluation
Abstract For the goal of automated design of high-performance deep convolutional neural networks (CNNs), neural architecture search (NAS) methodology is becoming increasingly important for both academia and industries. Due to the costly stochastic gradient descent training of CNNs for performance ev...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00594-5 |