Attention Based Model

Attention-based models are a class of deep learning architectures designed to process sequential data by selectively focusing on the most relevant parts of the input. Current research emphasizes improving efficiency, particularly addressing the quadratic complexity of standard attention mechanisms, through explorations of alternative architectures like state-space models (SSMs) such as Mamba and linear attention methods. These advancements are driving improvements in various applications, including natural language processing, computer vision, and time series analysis, by enhancing both accuracy and computational efficiency. The resulting models offer improved interpretability and robustness, leading to more reliable and explainable predictions across diverse domains.