Mamba: Linear-Time Sequence Modeling with Selective State Spaces解析 Mamba是基于SSM的改进,SSM介绍的文章见SSM介绍及离散化的过程paper: https://arxiv.org/pdf/2312.00752.pdfcode: https://github.com/state-spaces/mamba/tree/main
Mamba: Linear-Time Sequence Modeling with Selective State Spacesarxiv.org/abs/2312.00752 github:github.com/state-spaces Intro Mamba模型最近在深度学习领域掀起了不小的热潮,国内很多一部分研究者都在追赶这个热点,通用赛道的人想着把Transformer替换成Mamba刷个热点,具体赛道的想着哪个块能换成Mamba跑上一跑。
灵感来自经典状态空间模型。 这些模型可以被解释为循环神经网络(RNN)和卷积神经网络(CNN)的组合, 这类模型可以非常有效地进行递归或卷积计算,序列长度呈线性或近线性缩放。 优点: 在某些数据形式中具有建模长程依赖性的原理机制,并主导了诸如长程竞技场等基准测试。许多 SSMs 在涉及连续信号数据(如音频和视觉)的领域...
https://www.youtube.com/watch?v=9dSkvxS2EB0 OUTLINE: 0:00 - Introduction 0:45 - Transformers vs RNNs vs S4 6:10 - What are sttate space models? 12:30 - Selective State Space Models 17:55 - The Mamba architecture 22:20 - The SSM layer and forward propagation 31:15 - Utilizing...
@文心快码linear-time sequence modeling with selective state spaces 文心快码 作为Comate,我很高兴为你解答关于线性时间序列建模与选择性状态空间结合的问题。下面我将根据你的提示进行回答: 1. 解释线性时间序列建模的基本概念 线性时间序列建模是一种利用线性关系来预测未来时间点上数据值的方法。在这种方法中,未来...
Mamba: Linear-Time Sequence Modeling with Selective State Spaces Albert Gu*, Tri Dao* Paper:https://arxiv.org/abs/2312.00752 About Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall...
目录概Mamba代码 Gu A. and Dao T. Mamba: Linear-time sequence modeling with selective state spaces. 2023. 概 Mamba. Mamba S4 和 S4D 虽然解决了 SSM 计算速度的问题, 但是有一个前提
Mamba: Linear-Time Sequence Modeling with Selective State Spaces Winter 2024, CSE 291 (L00): Theory of LLMs, UC San Diego Deep learning applications have seen substantial advancements with the advent of the Transformer architecture and its attention mechanism. Despite its success, Transformers face ...
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