Mamba模型中，"A"、"B"、"C"和"D"分别代表状态空间模型（State Space Models，简称SSMs）的参数。这...
Section 2 State Space Models 状态空间模型 结构化状态空间序列模型(Structured state space sequence models,S4)是最近一类用于深度学习的序列模型,与 RNN、CNN 和经典状态空间模型广泛相关。它们受到一个特定连续系统 (1) 的启发,该系统通过一个隐含的潜在状态h(t)∈RNh(t)∈RN映射一个一维函数或序列x(t)∈R...
连续时间的状态空间模型continuous time state space sequence models (SSMs) 可以被表示为: h′(t)=Ah(t)+Bx(t) y(t)=Ch(t)+Dx(t) 其中x(t)∈R 是输入, y(t)∈R 是输出, h(t)∈RN 是隐藏状态变量。 而\mathbf{A}\in\mathbb{R}^{N\times N},\mathbf{B}\in\mathbb{R}^{N\times1...
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...
Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the ...
State Space Models (SSMs) have emerged as promising alternatives for sequence modeling paradigms, especially with the advent of S4 and its variants, such as S4nd, Hippo, Hyena, Diagonal State Spaces (DSS), Gated State Spaces (GSS), Linear Recurrent Unit (LRU), Liquid-S4, Long-Conv, Mega,...
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower bound. However, as we show, this does not ensure the model actually learns the underlying dynamics. We therefore ...
State–Space Models The study of state–space models has had a profound impact ontime seriesanalysis. A linear state–space model for a (possibly multivariate) time series {Yt,t= 1, 2, …} consists of two equations. The first, known as the observation equation, expresses thew-dimensionalob...
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....
论文:Simplified State Space Layers for Sequence Modeling要点:将多入多出状态空间模型引入 S4 层并将其与高效的并行扫描相结合,提出了新的 S5 层。 H3-attention language model 论文:Hungry Hungry Hippos: Towards Language Modeling with State Space Models要点:设计了一个新的 SSM 层 H3,几乎填平了 SSM 和...