Transformer Encoders

Transformer encoders are neural network architectures designed to process sequential data by leveraging self-attention mechanisms to capture long-range dependencies. Current research focuses on improving their efficiency, particularly for long sequences, through techniques like progressive token length scaling and optimized hardware acceleration, as well as exploring their expressivity and limitations in various applications. These advancements are driving significant improvements in diverse fields, including natural language processing, computer vision, and speech recognition, by enabling more accurate and efficient models for tasks such as machine translation, image segmentation, and speech diarization.