| Abstract: |
| Andrews plots provide lossless visualizations of high-dimensional data sets by linearly mapping data points to 1D functions. Employing the subtle nonlinear transformation which sends smooth functions to path integrals over a function`s graph, we obtain a neural network architecture admitting visualizations at every single step. In this talk, we demonstrate a universal approximation property of such networks, and discuss applications. |
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