  Common Neural Architecture Search methods generate large amounts of candidate
architectures that need training in order to assess their performance and find
an optimal architecture. To minimize the search time we use different
performance estimation strategies. The effectiveness of such strategies varies
in terms of accuracy and fit and query time. This study proposes a new method,
EmProx Score (Embedding Proximity Score). Similar to Neural Architecture
Optimization (NAO), this method maps candidate architectures to a continuous
embedding space using an encoder-decoder framework. The performance of
candidates is then estimated using weighted kNN based on the embedding vectors
of architectures of which the performance is known. Performance estimations of
this method are comparable to the MLP performance predictor used in NAO in
terms of accuracy, while being nearly nine times faster to train compared to
NAO. Benchmarking against other performance estimation strategies currently
used shows similar to better accuracy, while being five up to eighty times
faster.
