Trellis Networks for Sequence Modeling
Paper
•
1810.06682
•
Published
•
1
Convolution
-
-
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 -
Interpret Vision Transformers as ConvNets with Dynamic Convolutions
Paper • 2309.10713 • Published • 1 -
EfficientFormer: Vision Transformers at MobileNet Speed
Paper • 2206.01191 • Published • 1 -
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions
Paper • 2310.18780 • Published • 3 -
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper • 2312.04927 • Published • 2 -
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores
Paper • 2311.05908 • Published • 12 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 59 -
LKCA: Large Kernel Convolutional Attention
Paper • 2401.05738 • Published • 1 -
Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition
Paper • 2209.08326 • Published • 1 -
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
Paper • 2311.14495 • Published • 1 -
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 -
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Paper • 2310.19694 • Published • 2 -
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Paper • 2401.14168 • Published • 2 -
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency
Paper • 2311.02772 • Published • 3 -
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Paper • 2308.10110 • Published • 2 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 30 -
Simple Hardware-Efficient Long Convolutions for Sequence Modeling
Paper • 2302.06646 • Published • 2 -
Structured Pruning is All You Need for Pruning CNNs at Initialization
Paper • 2203.02549 • Published -
Graph Mamba: Towards Learning on Graphs with State Space Models
Paper • 2402.08678 • Published • 13 -
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
Paper • 2003.00152 • Published • 1 -
DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models
Paper • 2403.00818 • Published • 15 -
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Paper • 2403.19888 • Published • 10 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 51 -
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
Paper • 2402.18508 • Published -
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Paper • 2406.07522 • Published • 37 -
Deconvolutional Paragraph Representation Learning
Paper • 1708.04729 • Published -
ReMamba: Equip Mamba with Effective Long-Sequence Modeling
Paper • 2408.15496 • Published • 10