Modular Neural Network

Modular neural networks (MNNs) aim to improve the efficiency, interpretability, and generalization capabilities of neural networks by decomposing complex tasks into simpler sub-tasks handled by independent modules. Current research focuses on developing novel MNN architectures, such as those employing recurrent networks, attention mechanisms, and mixture-of-experts (MoE) models, and exploring both explicitly designed and implicitly emergent modularity within large language models. This approach holds significant promise for enhancing the performance and explainability of AI systems across diverse applications, including time series forecasting, robotics, and image recognition, while also addressing challenges like systematic generalization and efficient resource utilization.