Mamba Based Model

Mamba models are a novel class of deep learning architectures based on selective state-space models, offering a computationally efficient alternative to transformers for sequential data processing. Current research focuses on refining Mamba's architecture, exploring its application across diverse domains (including natural language processing, computer vision, and biomedical data analysis), and comparing its performance against established methods like transformers and convolutional neural networks. This work is significant due to Mamba's potential to improve the efficiency and scalability of large language models and other deep learning applications, particularly in resource-constrained environments.