Sep 23, 2023 · Our findings demonstrate that the addition of layer-wise nonlinear activation enhances the model's capacity to learn complex sequence patterns.

Sep 21, 2023 · This paper provides theory for sequence-to-sequence models based on state space layers, which have achieved state-of-the-art performance on a ...

Nov 1, 2023 · Figure 1: Network structure of two-layer state-space model. 2. The state-space models are shown to have an exponentially decaying memory, which.

State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory. Part of Advances in Neural Information ...

State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory. AUTHORs: Shida Wang and Beichen XueAuthors Info ...

It is proved that stacking state-space models with layer-wise nonlinear activation is sufficient to approximate any continuous sequence-to-sequence ...

State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory. S Wang, B Xue. Advances in Neural Information ...

7 days ago · State-space Models with Layer-wise Nonlinearity are Universal Approximators with ExponentialDecaying Memory. Authors: Shida Wang, Beichen Xue.

The authors show that the layer-wise nonlinearity is enough to achieve the universality when the state-space models are multi-layer. It is also shown that ...