Transformer-Evolution-Paper
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    • Accelerating Neural Transformer via an Average Attention Network
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    • Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries
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    • Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
    • Parallelizing Legendre Memory Unit Training
    • Simplified State Space Layers for Sequence Modeling
    • Pretraining Without Attention
    • What Makes Convolutional Models Great on Long Sequence Modeling?
    • Hungry Hungry Hippos: Towards Language Modeling with State Space Models
    • Hyena Hierarchy: Towards Larger Convolutional Language Models
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    • Simple Hardware-Efficient Long Convolutions for Sequence Modeling
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    • Towards a General Purpose CNN for Long Range Dependencies in ND
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    • When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
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    • Parameter-Efficient Fine-Tuning without Introducing New Latency
    • Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
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    • LLM Details Summary
    • What Language Model to Train if You Have One Million GPU Hours?
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  • 整体思路以及计算方式
  • 代码
  1. LongConv

What Makes Convolutional Models Great on Long Sequence Modeling?

PreviousPretraining Without AttentionNextHungry Hungry Hippos: Towards Language Modeling with State Space Models

Last updated 2 years ago

论文地址：

整体思路以及计算方式

这篇文章解释了S4性能好的原因：

  • 首先S4可以理解为全局卷积，卷积核是通过状态矩阵参数化；

  • S4之所以起作用是因为：

    • 卷积核的参数化和序列长度无关，否则很容易过拟合；

    • 局部先验，因为有矩阵Ak\mathbf A^kAk的形式；

这两点是S4真正起作用的原因，作者根据这两点使用了其他参数化的方式，也取得相当的结果。

代码

https://arxiv.org/abs/2210.09298
https://github.com/ctlllll/SGConv