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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 • 52
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 • 25
Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 99

Collection of State Space Model and Mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 132
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17 • 52
Vivim: a Video Vision Mamba for Medical Video Object Segmentation

Paper • 2401.14168 • Published Jan 25 • 1
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 • 25
Scavenging Hyena: Distilling Transformers into Long Convolution Models

Paper • 2401.17574 • Published Jan 31 • 14

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 • 25
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 47
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17 • 52

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 91
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 34
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 • 74
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

20s LLM Toolbox

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Paper • 2402.04291 • Published Feb 6 • 48
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 104
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 25
Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6 • 16

Grandmaster-Level Chess Without Search

Paper • 2402.04494 • Published Feb 7 • 65
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 25
Self-Play Preference Optimization for Language Model Alignment

Paper • 2405.00675 • Published May 1 • 19
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 58

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

Paper • 2402.04248 • Published Feb 6 • 25

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