Knowledge Enhanced

Knowledge-enhanced deep learning aims to improve the performance and robustness of neural networks by integrating external knowledge into their architectures and training processes. Current research focuses on developing methods for effectively incorporating diverse knowledge sources, such as knowledge graphs, symbolic rules, and even chain-of-thought prompting within large language models, into various neural network architectures, including multi-task networks and dynamic neural networks. This approach addresses limitations of traditional deep learning, such as data hunger and lack of interpretability, leading to improved accuracy, efficiency, and generalizability across numerous applications, including medical image analysis, music generation, and resource management in 6G networks.