By formulating architecture search as a linear program using gradient and curvature information, LP-NAS finds better neural network designs 2-3x faster than standard differentiable NAS methods while achieving higher accuracy.
This paper proposes LP-NAS, a neural architecture search method that uses linear programming to find better network designs faster. Instead of randomly exploring architectures, LP-NAS uses mathematical optimization principles to guide the search, resulting in architectures that generalize better and are found more quickly than existing methods like DARTS.