Transformer Network

Transformer networks are a class of deep learning models designed to process sequential data by leveraging self-attention mechanisms, enabling the capture of long-range dependencies within the data. Current research focuses on optimizing transformer architectures for efficiency and generalization, including exploring sparse connections, pruning techniques, and specialized hardware acceleration, as well as adapting them for diverse applications beyond natural language processing, such as image analysis, time series prediction, and signal processing. This versatility makes transformers a powerful tool across numerous scientific fields and practical applications, driving advancements in areas ranging from medical image analysis to autonomous driving and energy management.