Hybrid Neural Network

Hybrid neural networks combine the strengths of different neural network architectures or integrate neural networks with other computational methods, aiming to improve performance, efficiency, or interpretability in various applications. Current research focuses on developing hybrid models that incorporate elements like graph convolutional networks, spiking neural networks, and transformers, often coupled with physics-based models or other domain-specific knowledge. These advancements are impacting diverse fields, including energy management, credit risk assessment, medical image analysis, and time series forecasting, by enabling more accurate, efficient, and robust solutions to complex problems. The integration of diverse techniques within hybrid architectures is a key trend driving progress in machine learning and its applications.