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State Space Models

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/silvanmelchior/CBF-SSM

공식 코드

Structured Variational Inference in Unstable Gaussian Process State Space Models

https://github.com/simzhangbest/hmt-unet

공식 코드

HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation

https://github.com/siplab-gt/probabilistic-decomposed-linear-dynamical-systems

공식 코드

Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

https://github.com/sjblim/rnf-ijcnn-2020

공식 코드

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction

https://github.com/sjelassi/transformers_ssm_copy

공식 코드

Repeat After Me: Transformers are Better than State Space Models at Copying

https://github.com/skeletondyh/gred

공식 코드

Recurrent Distance Filtering for Graph Representation Learning

https://github.com/skyworkai/pointcloudmamba

공식 코드

Point Cloud Mamba: Point Cloud Learning via State Space Model

https://github.com/snel-repo/neural-data-transformers

공식 코드

Representation learning for neural population activity with Neural Data Transformers

https://github.com/state-spaces/mamba

공식 코드

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/steve-zeyu-zhang/MotionMamba

공식 코드

Motion Mamba: Efficient and Long Sequence Motion Generation

https://github.com/sustcsonglin/flash-linear-attention

공식 코드

Linear Transformers with Learnable Kernel Functions are Better In-Context Models

https://github.com/swjtuma/fer-yolo-mamba

공식 코드

FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space

https://github.com/terrypei/efficientvmamba

공식 코드

EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

https://github.com/tiancuteqy/endomamba

공식 코드

EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training

https://github.com/tianrun-chen/xLSTM-UNet-PyTorch

공식 코드

xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart

https://github.com/tmehari/ssm_ecg

공식 코드

Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models

https://github.com/tsy935/graphs4mer

공식 코드

Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models

https://github.com/tyshiwo1/dim-diffusionmamba

공식 코드

DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis

https://github.com/umbertocappellazzo/petl_ast

공식 코드

Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapters

https://github.com/ur17/em_mlgssm

공식 코드

Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models

https://github.com/uu-sml/neurips2019-parameter-elimination

공식 코드

Parameter elimination in particle Gibbs sampling

https://github.com/uzh-rpg/ssms_event_cameras

공식 코드

State Space Models for Event Cameras

https://github.com/vanderschaarlab/mlforhealthlabpub

공식 코드

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies

https://github.com/vdeborto/cdsb

공식 코드

Conditional Simulation Using Diffusion Schrödinger Bridges

https://github.com/vicgalle/vis

공식 코드

Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs

https://github.com/vinairesearch/dimsum

공식 코드

DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image Generation

https://github.com/vision-sjtu/quadmamba

공식 코드

QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model

https://github.com/vison307/mammil

공식 코드

MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models

https://github.com/vita-group/ssm-bottleneck

공식 코드

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

https://github.com/vmtenorio/prob-ssm

공식 코드

Tracking Network Dynamics using Probabilistic State-Space Models

https://github.com/wailordhe/densessm

공식 코드

DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models

https://github.com/wazizian/onlinesampling.jl

공식 코드

Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation

https://github.com/weitunglin/pixmamba

공식 코드

PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement

https://github.com/weixin-liang/mixture-of-mamba

공식 코드

Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity

https://github.com/windsornguyen/flash-stu

공식 코드

Flash STU: Fast Spectral Transform Units

https://github.com/winstonhutiger/mamba_mae

공식 코드

Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.