Dynamic Topology Dependency Learning (TDTL)

Abstract

This paper introduces Dynamic Topology Dependency Learning (TDTL), a novel approach to neural architecture search (NAS) that leverages the inherent dependencies within network topology to achieve more efficient architecture optimization. The core idea is to train a "topology controller" using reinforcement learning (RL) to dynamically suggest modifications to the network's connections – additions, deletions, or alterations – based on the network's current performance and structure. Unlike traditional NAS methods that rely on predefined search spaces or heuristics, TDTL learns directly from the network itself, enabling an adaptive and exploratory architecture optimization process. The learned topology dependencies are hypothesized to capture relationships between topological structure, task complexity, and data distribution characteristics. This approach presents a significant departure from existing NAS paradigms and holds the potential to unlock more effective and automated architecture discovery. The primary objective is to define a framework that learns the dependencies between neural network topology and its performance, ultimately leading to architectures optimized for specific tasks. ---