Dynamic Topology-Dependent Neural Architecture Search

Abstract

This paper introduces a novel approach to Neural Architecture Search (NAS) that leverages dynamic topology optimization based on dependency relationships. Traditional NAS methods often suffer from inefficiency and limited generalization due to the use of static search spaces and a lack of consideration for intricate layer-level dependencies within neural networks. Our method addresses these limitations by treating a neural network architecture as a graph, where nodes represent layers and edges represent dependencies. A dynamic search algorithm, guided by a dependency scoring function and topological constraints, iteratively modifies this graph's topology to explore a more efficient and effective search space. The core claim is that this dynamic approach, which explicitly models and optimizes topology, significantly improves both the efficiency and generalization capabilities of NAS. The algorithm incorporates a dependency scoring function that evaluates architecture changes based on dependency relationships and performance metrics, while topological constraints prevent drastic and potentially detrimental architectural alterations. The results demonstrate the effectiveness of this dynamic topology-dependent approach compared to conventional static NAS methods.