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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/lab-emi/cleanumamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/labpresse/biomodelcomparison

Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data

https://github.com/leonty1/deepldnn

Simplified State Space Layers for Sequence Modeling

https://github.com/leonty1/essm

Simplified State Space Layers for Sequence Modeling

https://github.com/linghao-jin/canmt-challenges

Mega: Moving Average Equipped Gated Attention

https://github.com/linjiefu-u/mamba_dose

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/lmd0311/pointmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/lucidrains/gated-state-spaces-pytorch

Long Range Language Modeling via Gated State Spaces

https://github.com/mzeromiko/vmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/necrashter/SaShiMi-796

It's Raw! Audio Generation with State-Space Models

https://github.com/nicolaszucchet/minimal-lru

Resurrecting Recurrent Neural Networks for Long Sequences

https://github.com/nl2g/quaterly-arxiv

NLLG Quarterly arXiv Report 09/24: What are the most influential current AI Papers?

https://github.com/nus-hpc-ai-lab/dyvm

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/nvlabs/convssm

Convolutional State Space Models for Long-Range Spatiotemporal Modeling

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

xLSTM: Extended Long Short-Term Memory

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

xLSTM: Extended Long Short-Term Memory

https://github.com/proroklab/popgym

On the Parameterization and Initialization of Diagonal State Space Models

https://github.com/pwc-1/Paper-9/tree/main/2/mega

Mega: Moving Average Equipped Gated Attention

https://github.com/radarFudan/Curse-of-memory

State-space Models with Layer-wise Nonlinearity are Universal Approximators with Exponential Decaying Memory

https://github.com/reeered/LRU-MindSpore

Resurrecting Recurrent Neural Networks for Long Sequences

https://github.com/rf5/diffwave-unconditional

It's Raw! Audio Generation with State-Space Models

https://github.com/rf5/simple-sashimi

It's Raw! Audio Generation with State-Space Models

https://github.com/rorschach1989/transformer-for-gmm

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

https://github.com/shendb2022/htd-mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/smvorwerk/xlstm-cuda

xLSTM: Extended Long Short-Term Memory

https://github.com/sustcsonglin/pytorch_linear_rnn

Resurrecting Recurrent Neural Networks for Long Sequences

https://github.com/thearkaprava/ms-temba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/tk-rusch/linoss

Oscillatory State-Space Models

https://github.com/togethercomputer/stripedhyena

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions

https://github.com/tommyip/mamba2-minimal

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/tonyyouyou/mamba-in-speech

Mamba in Speech: Towards an Alternative to Self-Attention

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

Parallelizing Linear Transformers with the Delta Rule over Sequence Length

https://github.com/uribyul/py_ai_clinician

Deep Reinforcement Learning for Sepsis Treatment

https://github.com/vertaix/Alpha-Alternator

The $α$-Alternator: Dynamic Adaptation To Varying Noise Levels In Sequences Using The Vendi Score For Improved Robustness and Performance

https://github.com/vvvm23/mamba-jax

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/weiwang31/icemamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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