Papers with Code 아카이브
Long-range modeling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 20
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/mivg/sled
공식 코드https://github.com/nankingjing/PoseMamba
공식 코드https://github.com/nathan66666/dvmsr
공식 코드DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
https://github.com/ogaloglu/pre-training-multilingual-document-encoders
공식 코드https://github.com/prismformore/multi-task-transformer
공식 코드TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts
https://github.com/pwangcs/hisvit
공식 코드Hierarchical Separable Video Transformer for Snapshot Compressive Imaging
https://github.com/qihao-duan/janusdna
공식 코드JanusDNA: A Powerful Bi-directional Hybrid DNA Foundation Model
https://github.com/qinsy123/2024-mambavc
공식 코드MambaVC: Learned Visual Compression with Selective State Spaces
https://github.com/radarFudan/mamba
공식 코드Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/raminmh/liquid-s4
공식 코드https://github.com/renll/seqboat
공식 코드https://github.com/ruslankhalitov/chordmixer
공식 코드ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
https://github.com/ruslankhalitov/sparsefactorization
공식 코드https://github.com/shannanyinxiang/viteraser
공식 코드https://github.com/shark-nlp/cab
공식 코드CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
https://github.com/sigeisler/s2gnn
공식 코드https://github.com/skeletondyh/gred
공식 코드Recurrent Distance Filtering for Graph Representation Learning
https://github.com/sstary/ssrs
공식 코드RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation
https://github.com/state-spaces/mamba
공식 코드Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/state-spaces/s4
공식 코드Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/tau-nlp/scrolls
공식 코드SCROLLS: Standardized CompaRison Over Long Language Sequences
https://github.com/tinghanf/regluarlrnn
공식 코드Advancing Regular Language Reasoning in Linear Recurrent Neural Networks
https://github.com/tsumansapkota/solving-lra-pathx
공식 코드Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation
https://github.com/vision-sjtu/quadmamba
공식 코드QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model
https://github.com/whwu95/DSANet
공식 코드DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
https://github.com/wiedersehne/paramixer
공식 코드https://github.com/wjh892521292/lkm-unet
공식 코드LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
https://github.com/wjh892521292/lma-unet
공식 코드LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
https://github.com/wujiong-hub/cdpdnet
공식 코드CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
https://github.com/xi-jia/lku-net
공식 코드U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
https://github.com/yingyichen-cyy/PrimalAttention
공식 코드Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
https://github.com/yunusa2k2/vGamba
공식 코드vGamba: Attentive State Space Bottleneck for efficient Long-range Dependencies in Visual Recognition
https://github.com/zy2219105/ct-mamba
공식 코드CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising
https://github.com/AmazingDay1/TAME
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/Efficient-Scalable-Machine-Learning/event-ssm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.