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Collections including paper arxiv:2402.04248

StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization

Paper • 2311.14495 • Published Nov 24, 2023 • 1
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17 • 58
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Paper • 2401.13560 • Published Jan 24 • 1
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Paper • 2402.00789 • Published Feb 1 • 2

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

Paper • 2401.09417 • Published Jan 17 • 58
VMamba: Visual State Space Model

Paper • 2401.10166 • Published Jan 18 • 37
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Paper • 2401.13560 • Published Jan 24 • 1
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Paper • 2402.00789 • Published Feb 1 • 2

new architecture

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 47
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 50
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
BlackMamba: Mixture of Experts for State-Space Models

Paper • 2402.01771 • Published Feb 1 • 23

StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization

Paper • 2311.14495 • Published Nov 24, 2023 • 1
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17 • 58
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Paper • 2401.13560 • Published Jan 24 • 1
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Paper • 2402.00789 • Published Feb 1 • 2

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 30
Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 104

Collection of State Space Model and Mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 138
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17 • 58
Vivim: a Video Vision Mamba for Medical Video Object Segmentation

Paper • 2401.14168 • Published Jan 25 • 2
HiPPO: Recurrent Memory with Optimal Polynomial Projections

Paper • 2008.07669 • Published Aug 17, 2020 • 1

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 30
Scavenging Hyena: Distilling Transformers into Long Convolution Models

Paper • 2401.17574 • Published Jan 31 • 15
Scalable Autoregressive Image Generation with Mamba

Paper • 2408.12245 • Published Aug 22 • 23
Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

Paper • 2408.12570 • Published Aug 22 • 29

Mambas and LLM-AltArch

Graph Mamba: Towards Learning on Graphs with State Space Models

Paper • 2402.08678 • Published Feb 13 • 13
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 30
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 50
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17 • 58

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 99
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 38
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

Paper • 2402.09727 • Published Feb 15 • 35

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 78
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

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