Efficient Forward Architecture Search - implementation -

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Efficient Forward Architecture Search by Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz and Debadeepta Dey

In this work, we propose a neural architecture search (NAS) algorithm that iteratively augments existing networks by adding shortcut connections and layers. At each iteration, we greedily select among the most cost-efficient models a parent model, and insert into it a number of candidate layers. To learn which combination of additional layers to keep, we simultaneously train their parameters and use feature selection techniques to extract the most promising candidates which are then jointly trained with the parent model. The result of this process is excellent statistical performance with relatively low computational cost. Furthermore, unlike recent studies of NAS that almost exclusively focus on the small search space of repeatable network modules (cells), this approach also allows direct search among the more general (macro) network structures to find cost-effective models when macro search starts with the same initial models as cell search does. Source code is available at https://github.com/microsoft/petridishnn. 

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