Reviews: DATA: Differentiable ArchiTecture Approximation

The paper takes the gumbel softmax trick in SNAS [1] further by ensembling the gumbel softmax estimator. As the result, it has a richer sample space while still being efficient. Rather than the credit assignment approach in SNAS, DATA makes use of the differentiability to update the probability vector. The paper is well written and clearly motivates the proposed approach. I am convinced that the proposed EGS estimator can bridge the gap of architectures between searching and validating, which is a well-known issue in DARTS [2]. The argument that the richer search space of EGS estimator is backed up by the experiments.