The fast conda package builder, based on mamba
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Updated
Jun 1, 2024 - Python
The fast conda package builder, based on mamba
A novel implementation of fusing ViT with Mamba into a fast, agile, and high performance Multi-Modal Model. Powered by Zeta, the simplest AI framework ever.
PointMamba: A Simple State Space Model for Point Cloud Analysis
Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images
A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model"
[IEEE TGRS 2024] ChangeMamba: Remote Sensing Change Detection Based on Spatio-Temporal State Space Model
Implementation of MambaByte in "MambaByte: Token-free Selective State Space Model" in Pytorch and Zeta
T-Mamba: Frequency-Enhanced Gated Long-Range Dependency for Tooth 3D CBCT Segmentation.
Recall to Imagine, a model-based RL algorithm with superhuman memory. Oral (1.2%) @ ICLR 2024
Official Implementation of the work "Audio Mamba: Bidirectional State Space Model for Audio Representation Learning"
Implementation of PyTorch: "GAMBA: MARRY GAUSSIAN SPLATTING WITH MAMBA FOR SINGLE-VIEW 3D RECONSTRUCTION"
An official implementation for SSAMBA: Self-Supervised Audio Mamba
Minimal Mamba-2 implementation in PyTorch
Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks.
Official implementation of I2I-Mamba, an image-to-image translation model based on selective state spaces
Min-K%++: Improved baseline for detecting pre-training data of LLMs https://arxiv.org/abs/2404.02936
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