## RobustDARTS

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**Understanding and Robustifying Differentiable Architecture Search**

Optimizing in the search of neural network architectures was initially defined as a discrete problem which intrinsically required to train and evaluate thousands of networks. This of course required huge amount of computational power, which was only possible for few institutions. One-shot neural architecture search (NAS) democratized this tedious search process by training only one large “supergraph” subsuming all possible architectures as its subgraphs. The NAS problem then boils down to finding the optimal path in this big graph, hence reducing the search costs to a small factor of a single function evaluation (one neural network training and evaluation). Notably, **D**ifferentiable **AR**chi**T**ecture **S**earch (**DARTS**) was widely appreciated in the NAS community due to its conceptual simplicity and effectiveness. In order to relax the discrete space to be continuous, DARTS linearly combines different operation choices to create a mixed operation. Afterwards, one can apply standard gradient descent to optimize in this relaxed architecture space. (more…)