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...
In the LH recordings, as described above, two-class and three-class (one-versus-rest multiclass classification72) models were computed using a nonlinear radial basis or linear kernel (depending on the dimensionality of the feature space). Linear SVMs were used to classify the population activity...
fMRI-S4 capture short- and long- range temporal dependencies in the signal using 1D convolutions and the recently introduced state-space models S4. The proposed architecture is lightweight, sample-efficient and robust across tasks/datasets. We validate fMRI-S4 on the tasks of diagnosing major ...
Second, the existing framework for state-space models made LDL more generally applicable and more flexible than DL. The LDL model could predict the performance of MCDI systems without increasing the model complexity. Also, it could predict the performance under time-varying voltages. The model makes...
A versatile energy ecosystem data space, the foundation of the energy metaverse. An interoperable virtual ecosystem living lab, the infrastructure of the energy metaverse An energy system models and AI algorithms sandbox, the construction of the energy metaverse ...
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,...
(B H L) # Compute D term in state space equation - essentially a skip connection y = y + u * self.D.unsqueeze(-1) y = self.dropout(self.activation(y)) y = self.output_linear(y) if not self.transposed: y = y.transpose(-1, -2) return y, None # Return a dummy state to ...
为了解决上面的问题,作者提出了一种新的选择性 SSM(Selective State Space Models,简称 S6 或 Mamba)。这种模型通过让 SSM 的矩阵 A、B、C 依赖于输入数据,从而实现了选择性。这意味着模型可以根据当前的输入动态地调整其状态,选择性地传播或忽略信息。 Mamba 集成了 S4 和 Transformer 的精华,一个更加高效(S4...
SSM指的是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有广泛联系。它们受到一个特殊连续系统的启发,该系统通过隐含的潜在状态h(t)∈RN,映射一个一维函数或序列x(t)∈R↦y(t)∈R。 S4模型可由四个参数(Δ,A,B,C)...
本文中的 diffusion model 采取的就是 S4 模型。 本文中最重要的贡献在于提出了Structured State Space Diffusion(SSSD) 架构,在这里简化为 SSSDS4 。并提出了针对于两种已有方法的 S4 改进版,分别是 non-autoregressive SaShiMi[5] 架构SSSDSA 以及使用 S4 改进后的 CSDI 架构[6] CSDIS4。 SSSDS4 架构如下...