(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 ...
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...
在S4模型之上,Liquid Structural State-space Models的工作引入了液体时间常数。 一个液体时间常数神经网络(Liquid Time-Constant Neural Network)[5]的通用的状态变化形式如下: \frac{d\bold x(t)}{dt} = -\underbrace{[\bold A + \bold B\odot f(\bold x(t), \bold u(t), t, \theta)]}_{Liquid...
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)...
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...
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State Space Models, and even the S4 (Structured State Space Model), perform poorly on certain tasks that are vital in language modeling and generation, namelythe ability to focus on or ignore particular inputs. We can illustrate this with two synthetic tasks, namelyselective copyingandinduction ...
Selective State Spaces就是个扩维的Gated linear RNN,跟Linear Attention有着千丝万缕的联系。 你说State Spaces离散化我笑.jpg。首先data dependent的decay完全丧失了LTI的性质,非要叫State Space多多少少有点强行。其次个人完全不信离散化能有什么用。如果真有用,论文实现里也不至于把B的离散化直接简化成linear ...
For a model with a finite state space, test cases would be generated directly from this machine, without needing further adjustment. However, since the test space of this sample model is infinite, it must be sliced in the Cord script before generating tests. The static model template provides...
We propose the Structured State Space sequence model (S4) based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning \( A \) with a low-rank ...