stereoplegic
's Collections
Convolution
updated
Trellis Networks for Sequence Modeling
Paper
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1810.06682
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Published
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1
Pruning Very Deep Neural Network Channels for Efficient Inference
Paper
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2211.08339
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Published
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1
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from
Scratch
Paper
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2309.14157
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Published
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1
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper
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2312.00752
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Published
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138
Interpret Vision Transformers as ConvNets with Dynamic Convolutions
Paper
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2309.10713
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Published
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1
EfficientFormer: Vision Transformers at MobileNet Speed
Paper
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2206.01191
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Published
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1
Laughing Hyena Distillery: Extracting Compact Recurrences From
Convolutions
Paper
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2310.18780
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Published
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3
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper
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2312.04927
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Published
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2
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor
Cores
Paper
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2311.05908
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Published
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12
Vision Mamba: Efficient Visual Representation Learning with
Bidirectional State Space Model
Paper
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2401.09417
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Published
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58
LKCA: Large Kernel Convolutional Attention
Paper
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2401.05738
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Published
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1
Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for
End-to-End Speech Recognition
Paper
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2209.08326
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Published
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1
StableSSM: Alleviating the Curse of Memory in State-space Models through
Stable Reparameterization
Paper
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2311.14495
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Published
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1
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image
Segmentation
Paper
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2401.13560
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Published
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1
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective
State Spaces
Paper
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2402.00789
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Published
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2
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Paper
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2310.19694
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Published
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2
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Paper
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2401.14168
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Published
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2
Attention or Convolution: Transformer Encoders in Audio Language Models
for Inference Efficiency
Paper
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2311.02772
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Published
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3
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Paper
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2308.10110
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Published
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2
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning
Tasks
Paper
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2402.04248
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Published
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30
Simple Hardware-Efficient Long Convolutions for Sequence Modeling
Paper
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2302.06646
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Published
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2
Structured Pruning is All You Need for Pruning CNNs at Initialization
Paper
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2203.02549
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Published
Graph Mamba: Towards Learning on Graphs with State Space Models
Paper
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2402.08678
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Published
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13
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random
Features in CNNs
Paper
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2003.00152
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Published
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1
DenseMamba: State Space Models with Dense Hidden Connection for
Efficient Large Language Models
Paper
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2403.00818
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Published
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15
MambaMixer: Efficient Selective State Space Models with Dual Token and
Channel Selection
Paper
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2403.19888
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Published
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10
MambaByte: Token-free Selective State Space Model
Paper
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2401.13660
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Published
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50
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
Paper
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2402.18508
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Published
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context
Language Modeling
Paper
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2406.07522
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Published
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37
Deconvolutional Paragraph Representation Learning
Paper
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1708.04729
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Published
ReMamba: Equip Mamba with Effective Long-Sequence Modeling
Paper
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2408.15496
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Published
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10