Currently employed neural network architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. This is when Neural architecture search, a subset of AutoML, came to the rescue. Neural Architecture Search (NAS) is the process of automating architecture engineering. Here we list top research works in Neural Architecture Search based on their popularity on Github. These works have set new baselines, resulted in new networks and more.

We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network's performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint.