Neural Architecture Search -- Limitations and Extensions


For the past couple of years, researchers and companies have been trying to make deep learning more accessible to non-experts by providing access to pre-trained computer vision or machine translation models. Using a pre-trained model for another task is known as transfer learning, but it still requires sufficient expertise to fine-tune the model on another dataset. Fully automating this procedure allows even more users to benefit from the great progress that has been made in ML to date. This is called AutoML, and it can cover many parts of predictive modelling such as architecture search and hyperparameter optimization. In this post, I focus on the former, as there has been a recent explosion of methods that search for the "best" architecture for a given dataset.

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