Intuitive Explanation of Differentiable Architecture Search (DARTS)

This is a paper that came out in the midst of 2018, addresses the problem of scalability of searching a network architecture. These papers address the problem of Neural Architecture Search or NAS in short. As the name suggests, the idea behind this field is to explore how can we automatically search deep learning model architectures. Currently, most of the data science problems are solved by manually designing the model architecture which gives "state of the art" results on any given dataset. The problem with this approach is that, though these architectures perform really good on the standard datasets, they don't perform as expected on the organisation specific datasets.