Neural Module

Neural modules are specialized sub-networks designed to perform specific computational tasks within a larger neural network, aiming to improve model interpretability, efficiency, and the ability to generalize to novel situations by combining learned modules. Current research focuses on developing effective module architectures (like convolutional blocks, transformers, and graph neural networks), learning algorithms for module specialization and composition, and training strategies such as curriculum learning and meta-learning to optimize module interactions. This modular approach holds significant promise for enhancing the performance and explainability of complex AI systems, particularly in areas like visual question answering and clinical decision support, where interpretability and adaptability are crucial.