自从Mamba问世以来,只知道有这么一个可以和transformer媲美的模型,却一直没有学习过其中的方法、概念。这两天查阅了一些资料、博客,自觉把State Space Model和Mamba中的关键点概念性地理解且串起来了。各种细节还没有深究,但前因后果,high-level层面的理解肯定可以有逻辑地讲出来了。这篇文章就作为一篇学习笔记总结了Ma...
SSM(State Space Model,状态空间模型)是一种用于描述时间序列数据的统计模型。它广泛应用于机器学习和统计学中,用于处理动态系统和时变过程。SSM可以捕捉系统状态随着时间的变化,以及观察到的数据与这些状态之间的关系。 SSM的基本构成 状态空间表示 状态空间模型由两个主要部分组成: 状态方程(State Equation):描述系统...
if flag == 'test': shuffle_flag = False drop_last = True batch_size = args.b...
LegT 度量为最近的历史信息分配均匀的权重,表示如下: \textbf{LegT: }\space \mu^{\left( t \right)}\left( x \right)=\frac{1}{\theta} \mathbb{I}_{\left[ t-\theta,t \right]}\left( x \right) \tag7 LagT 度量使用指数衰减的方式来衡量历史信息的重要性,表示如下: \textbf{LagT: }\s...
MambaIR: A Simple Baseline for Image Restoration with State-Space Model [Paper] [Zhihu(知乎)] Hang Guo*,Jinmin Li*,Tao Dai, Zhihao Ouyang, Xudong Ren, andShu-Tao Xia (*) equal contribution Abstract:Recent years have witnessed great progress in image restoration thanks to the advancements in...
MambaIR: A Simple Baseline for Image Restoration with State-Space Model [Paper] [Zhihu(知乎)] Hang Guo*, Jinmin Li*, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia Check our paper collection of recent Awesome Mamba work in Low-Level Vision [here] 🤗. (*) equal contribution Ab...
本文提出了一种新的架构——VMamba(Visual State Space Model),继承了CNNs和ViTs的优点,同时还提高了计算效率,在不牺牲全局感受野的情况下可以达到线性复杂度。为了解决方向敏感问题,引入了交叉扫描模块( Cross-Scan Module,CSM )来遍历空间域,并将任何非因果的视觉图像转换为有序的块序列。VMamba不仅在各种视觉...
REST APIs useUniform Resource Identifiers(URIs) to address resources. REST API designers should create URIs that convey a REST API’s resource model to its potential client developers. When resources are named well, an API is intuitive and easy to use. If done poorly, that same API can feel...
(*^^*)~~还是转自★【SHCN神话中国】★论坛~芊芊~亲~~谢谢亲的提 分享49赞 pitchfork吧 MMaverick 【Pitchfork】Decade in Music 分享14赞 matlab吧 夜景漫长 请问这种问题怎么解决Derivative of state '1' in block 'untitled1/powergui/EquivalentModel1/State-Space' at time 0.00011478697201820633 is not ...
Additive and multiplicative attention are similar in complexity, although multiplicative attention is faster and more space-efficient in practice as it can be implemented more efficiently using matrix multiplication. http://ruder.io/deep-learning-nlp-best-practices/index.html#fn35 ...