Thus we get the searched architecture and optimized parameters in a single run.

Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods commonly ignore the constraint of sparsity in the search phase, but project the optimized solution onto a sparse one by post-processing. As a result, the dense super-net for search is inefficient to train and has a gap with the projected architecture for evaluation. In this paper, we formulate neural architecture search as a sparse coding problem. We perform the differentiable search on a compressed lower-dimensional space that has the same validation loss as the original sparse solution space, and recover an architecture by solving the sparse coding problem.

I could not see a strong motivation for explicitly enforcing sparsity on architecture parameters. This is because there are already many works trying to decouple the dependency of evaluating sub-networks on the training of supernet (i.e., making the correlation higher). This means that we have ways to explicitly decouple the network evaluation with supernet training without adding a sparsity regularizaiton. As far as I know, weight-sharing methods require the BN to be re-calculated [1] to properly measure the Kendall correlation. Other works that can reduce the gap between supernet and sub-networks (e.g.

Four knowledgeable reviewers support acceptance for the contributions. Reviewers find that i) using sparse coding to solve the gap issue in NAS is novel and promising. The formulation and notations are neat. There is also a performance improvement in the one-stage framework. V) the paper is well-organized and easy to understand.

Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods commonly ignore the constraint of sparsity in the search phase, but project the optimized solution onto a sparse one by post-processing. As a result, the dense super-net for search is inefficient to train and has a gap with the projected architecture for evaluation. In this paper, we formulate neural architecture search as a sparse coding problem. We perform the differentiable search on a compressed lower-dimensional space that has the same validation loss as the original sparse solution space, and recover an architecture by solving the sparse coding problem.