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Collections including paper arxiv:2402.04248

State Space Models

Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 25
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 74
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss

Paper • 2402.10790 • Published Feb 16 • 40

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Paper • 2401.15688 • Published Jan 28 • 10
SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Paper • 2401.15024 • Published Jan 26 • 62
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Paper • 2401.15071 • Published Jan 26 • 33

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 • 51
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

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15
Transforming and Combining Rewards for Aligning Large Language Models

Paper • 2402.00742 • Published Feb 1 • 11
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5 • 64
Specialized Language Models with Cheap Inference from Limited Domain Data

Paper • 2402.01093 • Published Feb 2 • 45

BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation

Paper • 2401.17053 • Published Jan 30 • 29
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 25
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5 • 64
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Paper • 2402.05930 • Published Feb 8 • 35

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 50
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 18
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 5

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16 • 19
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10 • 62

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

Paper • 2401.09417 • Published Jan 17 • 51
VMamba: Visual State Space Model

Paper • 2401.10166 • Published Jan 18 • 36
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

about 13 hours ago

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

Paper • 2401.02994 • Published Jan 4 • 45
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 47
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 • 22

A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation

Paper • 2310.16656 • Published Oct 25, 2023 • 37
Unsupervised Universal Image Segmentation

Paper • 2312.17243 • Published Dec 28, 2023 • 18
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

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