This repository serves as a comprehensive, curated collection of resources for Mamba, a novel sequence modeling architecture, and related State Space Models (SSMs). It targets researchers, developers, and practitioners seeking to understand and apply Mamba across various domains, offering a centralized hub for papers, tutorials, videos, and code implementations.

How It Works

The collection is organized by application area, indexing a vast array of research papers, tutorials, blog posts, videos, and code repositories related to Mamba. This categorization allows users to efficiently discover resources relevant to specific fields such as computer vision, natural language processing, medical imaging, time series analysis, and reinforcement learning, among many others.

Quick Start & Requirements

While the collection itself requires no installation, accessing Mamba implementations varies. The official Mamba repository is noted as being Linux-only. However, the collection links to minimal PyTorch implementations (e.g., mamba-minimal, mamba2-minimal, mamba.py) and a C/CUDA inference version (mamba.c), providing options for experimentation.

Highlighted Details

• Broad Application Spectrum: Covers Mamba's use in core architecture, theoretical analysis, and diverse applications including Vision, Language, Multi-Modal, Spatio-Temporal, Diffusion, Medical, Tabular Data, Graph, Point Cloud, Time Series, Speech, Recommendation, and Reinforcement Learning.
• Resource Variety: Includes links to research papers with associated code and official project pages, alongside tutorials, blogs, and videos explaining Mamba and SSM concepts.
• Implementation Options: Provides pointers to various Mamba implementations, catering to different needs from minimal PyTorch versions to efficient C/CUDA inference.
• Community Contribution: Actively encourages community involvement through contributions to the README, fostering a collaborative knowledge-sharing environment.

Maintenance & Community

The repository welcomes community contributions, outlining a clear three-step process: Fork, Edit Content, and Create a Pull Request. Contributors are encouraged to follow existing formatting and ensure the relevance and accuracy of added resources.

Licensing & Compatibility

The provided README text does not specify a software license for the collection itself. Users should refer to the individual linked repositories for their respective licenses and compatibility terms.

Limitations & Caveats

A notable limitation mentioned is that the official Mamba repository is currently restricted to Linux environments. The utility of the collection is dependent on the accessibility and maintenance of the external resources it links to.