Dynamic Topological Adaptive Neural Architecture Search

Abstract

This paper proposes a novel approach to Neural Architecture Search (NAS) termed Dynamic Topological Adaptive NAS (DTANAS), which leverages concepts from topological data analysis and physical systems to achieve more efficient and robust architecture optimization. The core idea is to model the search space as a complex topological network, allowing for dynamic changes in architecture during the training process. We introduce a framework where architectural modifications are viewed as topological transformations, induced by random perturbations and feedback mechanisms. These mechanisms mimic the adaptive behavior observed in physical systems undergoing evolution and self-organization. The system employs an evolutionary or reinforcement learning strategy to evaluate and refine the 'topological quality' of the network architecture. Unlike traditional NAS methods that rely on static search spaces and gradient information, DTANAS offers a fundamentally different perspective, potentially leading to architectures that are more resilient to noise and better suited for complex, evolving tasks. This work presents a theoretical framework and a computational approach to explore this paradigm, demonstrating the potential for significant improvements in NAS efficiency and architectural quality.