Скачать или смотреть Why care about Neural Architecture Search (NAS)?

In this video, we'll learn an overview of Neural Architecture Search (NAS) and why you should care about it! We'll discuss the motivation behind NAS, NAS approaches (reinforcement learning, neuroevolution, DARTS, one-shot NAS, and training-free methods), and NAS benchmarks as well.

TIMESTAMPS:
0:00 Intro
0:15 Motivation
0:41 Traditional vs. NAS
1:01 Search methods
2:52 Reproducibility problem
3:41 NAS benchmarks
4:05 Other applications
4:18 Why care?
5:10 Summary

Image Credits:
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References:
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Gridin, I. (2022). One-Shot Neural Architecture Search. In Automated Deep Learning Using Neural Network Intelligence: Develop and Design PyTorch and TensorFlow Models Using Python (pp. 257-318). Berkeley, CA: Apress.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Klyuchnikov, N., Trofimov, I., Artemova, E., Salnikov, M., Fedorov, M., Filippov, A., & Burnaev, E. (2022). Nas-bench-nlp: neural architecture search benchmark for natural language processing. IEEE Access, 10, 45736-45747.
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