General Transformer

General transformers are a class of neural network architectures designed to process sequential data, achieving remarkable success across diverse applications. Current research focuses on improving their generalization capabilities, particularly for longer sequences and unseen data, exploring novel attention mechanisms and model architectures like decoder-only transformers and complementary transformers to enhance efficiency and performance. This work is significant because it addresses limitations in existing models, leading to more robust and adaptable systems with applications ranging from robot control and medical diagnosis to image processing and brain network analysis.