https://github.com/Fantasyele/LLaVA-KD
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
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/Jiaxin-Ye/DepMamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/KyanChen/RSMamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/Leeroo-AI/mergoo
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/MalongTech/research-v4d
V4D:4D Convolutional Neural Networks for Video-level Representation Learning
https://github.com/MindCode-4/code-2/tree/main/pegasus_x
Investigating Efficiently Extending Transformers for Long Input Summarization
https://github.com/MindSpore-scientific-2/code-14/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/ZIZUN/MAFiD
https://github.com/aiot-mlsys-lab/famba-v
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/anzeyimana/deepkin
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/areffarhadi/mamba-asr
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/author31/scm_nbdev
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
https://github.com/catalpaaa/demansia
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/dar-tau/lra-benchmark
https://github.com/elgazzarr/fmri-s4
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/ethanbar11/ssm_2d
https://github.com/forgi86/lru-reduction
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/google-research/bigbird
https://github.com/guyd1995/lra-benchmark
https://github.com/guyd1995/lra-benchmarks
https://github.com/haiyang-w/tokenformer
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/havenhq/mamba-chat
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/hazdzz/Paramixer
https://github.com/hazyresearch/safari
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
https://github.com/huggingface/transformers
https://github.com/human9000/nd-mamba2-torch
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/hustvl/vim
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/irmvlab/point-mamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/jindongjiang/slotssms
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/johnma2006/mamba-minimal
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/jsie7/ssm-benchmark
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/kaviezhang/meshmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/kennymckormick/pyskl
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
https://github.com/khfs/DuplexMamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/lab-emi/cleanumamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/leonty1/deepldnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.