Attention Layer

Attention layers are fundamental components of neural networks, particularly transformers, designed to selectively focus on relevant information within input data. Current research emphasizes improving attention's efficiency and theoretical understanding, exploring variations like sparse, hyperbolic, and grouped query attention within models such as transformers, and investigating the interplay between attention and other layers (e.g., convolutional, MLP). This work is crucial for advancing the capabilities of large language models and other deep learning architectures, impacting diverse applications from image generation and compression to natural language processing and even seismic analysis.