This repository contains a collection of resources and papers on Mamba Models for different domains. We are pleased to see that more and more efforts have been devoted to dealing with structured state space models since 2023.
If you have any relevant paper or codes to update the list, please pull a request or report an issue.
Mamba is a new state-space model architecture showing promising performance on language modeling with O(N) complexity.
mamba.py 🐍 : a simple and efficient Mamba implementation
Efficient Visual Representation Learning with Bidirectional State Space Model
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
VMamba: Visual State Space Model
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
MambaByte: Token-free Selective State Space Model
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
BlackMamba: Mixture of Experts for State-Space Models
Repeat After Me: Transformers are Better than State Space Models at Copying
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
LOCOST: State-Space Models for Long Document Abstractive Summarization
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
Graph Mamba: Towards Learning on Graphs with State Space Models