[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".

Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis

[ACM MM'24 Oral] RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining

Official repository for Mamba-based Segmentation Model for Speaker Diarization

A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration, and Beyond

Official implementation of I2I-Mamba, an image-to-image translation model based on selective state spaces

Major Research Paper for M.Sc. in Data Science at TMU

[WACV2025] SUM: Saliency Unification through Mamba for Visual Attention Modeling

MambaMIM: Pre-training Mamba with State Space Token-interpolation

A simple analysis of telephone pulses using FFT, CNN and MAMBA.

Welcome to the world of Mamba! This repository is a curated collection of papers, tutorials, videos, and other valuable resources related to Mamba.

This evaluation explores the In-context learning (ICL) capabilities of pre-trained language models on arithmetic tasks and sentiment analysis using synthetic datasets. The goal is to use different prompting strategies—zero-shot, few-shot, and chain-of-thought—to assess the performance of these models on the given tasks.

Segmentation of cancerous tumors using Mamba. Code, resources, and paper provided. We manage to make a small (42k param) model that can segment pretty well.

Library for Federated Emergence & Foundation Models