NASGraph: A Novel Graph-based Machine Learning Method for NAS Featuring Lightweight (CPU-only) Computation and is Data-Agnostic and Training-Free

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● Designing state-of-the-art deep learning models through Neural Architecture Search (NAS) is a complex challenge.
● Previous NAS methods faced bottlenecks due to computationally expensive and time-consuming training.
● NASGraph efficiently reduces the computational burden of neural architecture search by converting architectures into graph representations and using graph metrics.
● NASGraph utilizes surrogate models with reduced computational requirements to accelerate the architecture ranking process.
● NASGraph outperforms previous training-free NAS methods, exhibits low bias, and achieves state-of-the-art rankings, paving the way for rapid neural architecture exploration.

Author: Vineet Kumar
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