Modular Network

Modular networks, neural network architectures composed of interconnected, specialized sub-networks (modules), aim to improve performance and interpretability by leveraging the compositional nature of many real-world problems. Current research focuses on developing efficient training methods for these networks, exploring their generalization capabilities in high-dimensional spaces, and investigating the relationship between modularity and interpretability, often employing techniques like low-rank adaptation and curriculum learning. This approach holds significant promise for enhancing the efficiency, robustness, and explainability of artificial intelligence systems across diverse applications, from robotics and natural language processing to federated learning and medical image analysis.