Dynamic Topology-Dependent Neural Network Optimization

Abstract

This paper presents a novel approach to neural network optimization that dynamically adapts the network topology based on learned dependencies. The core idea is to leverage reinforcement learning, where two agents collaborate: one adjusts connection weights and the other modifies the network's structure (adding, removing, or restructuring connections). A 'dependency graph' guides the agents' decisions, reflecting the learned information dependencies between neurons. This dynamic adaptation addresses the limitations of traditional methods that assume a fixed network topology, particularly when dealing with complex dependencies and non-Euclidean data. The system aims to achieve more efficient training and improved generalization performance by allowing the network to evolve its structure to better represent the underlying data. The optimization process is driven by minimizing a loss function, and the dependency graph is continuously updated based on the error signal. The key contribution lies in the integration of topology adaptation with reinforcement learning, providing a framework for creating inherently adaptive and robust neural networks. The proposed methodology demonstrates potential for significant improvements in network performance across various domains.