Keywords: Mamba, State Space Models, Graph Neural NetworksIntroductionGTs相对于MPNN的优势通常可以解释为MPNN倾向于编码局部结构,而GTs的一个关键基本原则是让节点通过全局注意机制关注所有其他节点,允许直…
Recently, State-Space Models (SSMs), exemplified by Mamba, have gained significant attention as a promising alternative due to their linear computational complexity. Another approach, neural memory Ordinary Differential Equations (nmODEs), exhibits similar principles and achieves good results. In this ...
原文地址：https://pub.towardsai.net/understanding-mamba-and-selective-state-space-models-ssms-1519c...
3 Selective State Space Models We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how...
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and discretize them to handle input data, making them well-suited for modeling time series data collected at specific freq...
状态空间模型(State Space Models) 状态空间模型(SSMs)通常被认为是将刺激 映射到响应 的线性时不变系统。从数学上讲,这些模型通常被构建为线性常微分方程(ODEs): ,其中 , 、 , 为状态大小,以及跳跃连接 。 离散化(Discretization) 没看懂,后来再看一遍。
To address this challenge, State Space Models (SSMs) like Mamba have emerged as efficient alternatives, initially matching Transformer performance in NLP tasks and later surpassing Vision Transformers (ViTs) in various CV tasks. To improve the performance of SSMs, one crucial aspect is effective ...
2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms ...
State space models (SSMs) offer a more efficient alternative to transformers. SSMs scale with linear complexity, making them significantly faster and more memory-efficient for long sequences. However, SSMs are limited in recalling information and often underperform compared to transformers, especially on...
is standalone, and can be used for any sequence modeling problem, one does not use by default this formulation where we carry on the hidden state. The implementation is the same as the original JAX implementation and can be downloaded in zip format fromssms_event_cameras/RVT/models/s5.zip....