https://github.com/leonty1/essm
Efficiently Modeling Long Sequences with Structured State Spaces
Papers with Code 아카이브
Long-range modeling
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A new task for testing the long-sequence modeling capabilities and efficiency of language models.
Image credit: SCROLLS: Standardized CompaRison Over Long Language Sequences
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/linghao-jin/canmt-challenges
https://github.com/lmd0311/pointmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/lucidrains/gated-state-spaces-pytorch
https://github.com/maxtimer97/ssm-inspired-lif
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/metrics-lab/st-fmri
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
https://github.com/msgwak/last
https://github.com/mzeromiko/vmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/nicolaszucchet/minimal-lru
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/nus-hpc-ai-lab/dyvm
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/opennlg/openba-v2
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
https://github.com/pwc-1/Paper-9/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/shendb2022/htd-mamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/srush/annotated-s4
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/thearkaprava/ms-temba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/tommyip/mamba2-minimal
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/utsjiyaoli/qa-attack
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/uzh-rpg/ssms_event_cameras
https://github.com/vvvm23/mamba-jax
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/weitunglin/pixmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/weiwang31/icemamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/xi-j/mamba-asr
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/yangyucheng000/Paper-2/tree/main/cns
CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal
https://github.com/zhangyezhuo/mamca
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/zhangzjn/ader
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
https://github.com/zhu-0108/mts-unmixers
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/ziyangwang007/vmambamorph
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
https://github.com/zyphra/blackmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/zzmtsvv/mamba-interface
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.