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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 140 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 10 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 48 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 44
Collections
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Collections including paper arxiv:2401.13660
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StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
Paper • 2311.14495 • Published • 1 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 56 -
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Paper • 2401.13560 • Published • 1 -
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
Paper • 2402.00789 • Published • 2
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Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 56 -
VMamba: Visual State Space Model
Paper • 2401.10166 • Published • 37 -
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Paper • 2401.13560 • Published • 1 -
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
Paper • 2402.00789 • Published • 2
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Trellis Networks for Sequence Modeling
Paper • 1810.06682 • Published • 1 -
Pruning Very Deep Neural Network Channels for Efficient Inference
Paper • 2211.08339 • Published • 1 -
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Paper • 2309.14157 • Published • 1 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138
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XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Paper • 2404.15420 • Published • 7 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 124 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 250 -
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Paper • 2404.14047 • Published • 43
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ByT5: Towards a token-free future with pre-trained byte-to-byte models
Paper • 2105.13626 • Published • 2 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
Paper • 2305.07185 • Published • 9 -
Byte-Level Recursive Convolutional Auto-Encoder for Text
Paper • 1802.01817 • Published
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 56 -
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Paper • 2401.14168 • Published • 1 -
HiPPO: Recurrent Memory with Optimal Polynomial Projections
Paper • 2008.07669 • Published • 1
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Graph Mamba: Towards Learning on Graphs with State Space Models
Paper • 2402.08678 • Published • 13 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 28 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 48 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 56