Sequence Transformer

Sequence transformers are neural network architectures designed to process sequential data, aiming to improve efficiency and accuracy in various applications. Current research focuses on optimizing these models for handling extremely long sequences, incorporating techniques like sequence partitioning and activation recomputation to reduce memory demands, and exploring hybrid approaches combining transformers with other architectures like recurrent neural networks (RNNs) to enhance performance. This work has significant implications across diverse fields, including natural language processing, computer vision (e.g., image segmentation and facial recognition), and speech recognition, leading to advancements in tasks such as text normalization, handwritten text recognition, and surgical step detection.