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Robustifying State-space Models for Long Sequences via Approximate Diagonalization
An example is the structured state-space sequence (S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO initialization framework.
Tuning Frequency Bias in Neural Network Training with Nonuniform Data
Small generalization errors of over-parameterized neural networks (NNs) can be partially explained by the frequency biasing phenomenon, where gradient-based algorithms minimize the low-frequency misfit before reducing the high-frequency residuals.
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks
Here, we prove that operator NNs of bounded width and arbitrary depth are universal approximators for continuous nonlinear operators.
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