#ai #research #machinelearning
Neural Architecture Search is typically very slow and resource-intensive. A meta-controller has to train many hundreds or thousands of different models to find a suitable building plan. This paper proposes to use statistics of the Jacobian around data points to estimate the performance of proposed architectures at initialization. This method does not require training and speeds up NAS by orders of magnitude.
OUTLINE:
0:00 – Intro & Overview
0:50 – Neural Architecture Search
4:15 – Controller-based NAS
7:35 – Architecture Search Without Training
9:30 – Linearization Around Datapoints
14:10 – Linearization Statistics
19:00 – NAS-201 Benchmark
20:15 – Experiments
34:15 – Conclusion & Comments
Paper:
Code:
Abstract:
The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be extremely slow and expensive; they need to train vast numbers of candidate networks to inform the search process. This could be remedied if we could infer a network’s trained accuracy from its initial state. In this work, we examine how the linear maps induced by data points correlate for untrained network architectures in the NAS-Bench-201 search space, and motivate how this can be used to give a measure of modelling flexibility which is highly indicative of a network’s trained performance. We incorporate this measure into a simple algorithm that allows us to search for powerful networks without any training in a matter of seconds on a single GPU. Code to reproduce our experiments is available at this https URL.
Authors: Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley
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