#Mamba

This catalog provides a wide range of Mamba-related resources, including papers, tutorials, and videos for all user levels. It acts as a detailed reference point for understanding Mamba, integrating recent research, practical guides, and instructional videos. This resource encourages community collaboration to boost Mamba skills and promote shared learning. Topics covered range from architecture and theoretical analysis to practical applications in vision and language.

This collection of Mamba-focused computer vision projects highlights recent developments including human activity recognition, anomaly detection, and autonomous driving. It explores the capabilities of state space models as alternatives to transformers, providing links to detailed papers and code. Ideal for researchers and practitioners interested in visual state space models.

The Mamba project provides an innovative state space model architecture designed for efficient handling of information-dense data such as language models, overcoming the shortcomings of earlier subquadratic models. Its architecture, focusing on hardware efficiency similar to FlashAttention, utilizes selective state space modeling for scalable solutions. Pretrained models are available on Hugging Face, and the `lm-evaluation-harness` library enables evaluations. Comprehensive resources include installation guides, usage instructions, and benchmarking scripts to support seamless integration and performance optimization.

This resource provides a comprehensive list of papers dedicated to State-Space Models (SSMs) and their applications, emphasizing their potential as alternatives to Transformers in innovative network solutions. It encompasses detailed theoretical insights and practical applications, including visual object tracking, 3D pose estimation, and medical image processing. The collection boasts thorough surveys, theses references, and the latest research publications, serving as a valuable tool for academics and practitioners looking to keep pace with the evolving role of state-space modeling. It is regularly updated to reflect the latest progressions in the field.