Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g.

This paper proposes a formal langauge to describe the search space of architecture search problem. This langauge is a domain specific language embedded in python. Users can write modular, composable, and reusable search space by using this langauge. Originality: The contribution is new. This is the first work that tries to provide a formal langauge for the space definition.

The authors should be commended for submitting a clear and timely paper on the subject of neural architecture search. The establishment of a formal language describing architectures that allows separation of problem from solution was deemed sufficiently novel to warrant publication.

Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g. Hyperparameter optimization systems are general-purpose but lack the constructs needed for easy application to architecture search. In this work, we propose a formal language for encoding search spaces over general computational graphs. The language constructs allow us to write modular, composable, and reusable search space encodings and to reason about search space design.

Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g. Hyperparameter optimization systems are general-purpose but lack the constructs needed for easy application to architecture search. In this work, we propose a formal language for encoding search spaces over general computational graphs. The language constructs allow us to write modular, composable, and reusable search space encodings and to reason about search space design.

To appear at NeurIPS 2019. Modular and programmable architecture search framework that allows you to implement your own search spaces and search algorithms through a consistent API. Reading the Twitter thread will give you a pretty good idea of the main ideas. Looking to get a few initial users and feedback.