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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/nx-ai/tirex

공식 코드

TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning

https://github.com/nx-ai/xlstm

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xLSTM: Extended Long Short-Term Memory

https://github.com/opennlplab/etsc-exact-toeplitz-to-ssm-conversion

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Accelerating Toeplitz Neural Network with Constant-time Inference Complexity

https://github.com/opensparsellms/linear-moe

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Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts

https://github.com/orange-czh/mamba-sea

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

https://github.com/oronsabag/regret-optimal-filtering

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Regret-Optimal Filtering for Prediction and Estimation

https://github.com/pbecker93/vrkn

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On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

https://github.com/pearsonlab/spiketopics

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Neuron's Eye View: Inferring Features of Complex Stimuli from Neural Responses

https://github.com/peernagy/lob_bench

공식 코드

LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data

https://github.com/pessoap/mamba-probtsf

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Mamba time series forecasting with uncertainty quantification

https://github.com/pfrommerd/variational_state_space_models

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Linear Variational State-Space Filtering

https://github.com/pixdeep/mhs-vm

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MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba

https://github.com/pkuai-lingroup/etsmlp

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Incorporating Exponential Smoothing into MLP: A Simple but Effective Sequence Model

https://github.com/pure-melo/s2mamba

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S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification

https://github.com/qglht/repal

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Dynamical similarity analysis can identify compositional dynamics developing in RNNs

https://github.com/qinsy123/2024-mambavc

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MambaVC: Learned Visual Compression with Selective State Spaces

https://github.com/qiuzh20/gated_attention

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Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

https://github.com/qu-gg/causal-effect-neural-ssm

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Neural State-Space Modeling with Latent Causal-Effect Disentanglement

https://github.com/radarFudan/mamba

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/raminmh/liquid-s4

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Liquid Structural State-Space Models

https://github.com/raunaqbhirangi/hiss

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Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling

https://github.com/raytrun/mamba-clip

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CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation

https://github.com/remosasso/psdrl

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Posterior Sampling for Deep Reinforcement Learning

https://github.com/renll/seqboat

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Sparse Modular Activation for Efficient Sequence Modeling

https://github.com/riccardovib/optical-drc-with-selective-ssms

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Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models

https://github.com/ruke1ire/RTF

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State-Free Inference of State-Space Models: The Transfer Function Approach

https://github.com/ryoichi0917/svi_mssm

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Time Series Clustering with General State Space Models via Stochastic Variational Inference

https://github.com/samblouir/birdie

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Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula

https://github.com/sascha-kirch/samba-mixer

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SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models

https://github.com/satyajitneogiju/FLDCRF-for-sequence-labeling

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Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence Modeling

https://github.com/scaledfoundations/matmamba

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MatMamba: A Matryoshka State Space Model

https://github.com/scnu-rislab/overlapmamba

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OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition

https://github.com/scott-yjyang/vivim

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Vivim: a Video Vision Mamba for Medical Video Segmentation

https://github.com/shenshuaijie/SDN

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SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models

https://github.com/shim0114/ssm-meets-video-diffusion-models

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SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces

https://github.com/siavashshams/ssamba

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SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model

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