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arXiv · 2010.01381

Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences

Jing Shi·Jing Bi·Yingru Liu·Chenliang Xu
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Abstract

The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth hidden states between observation intervals, the RNN will trigger a hidden state jump when a new observation arrives, thus cause the interpolation discontinuity problem. To address this issue, we propose the cubic spline smoothing compensation, which is a stand-alone module upon either the output or the hidden state of ODE-RNN and can be trained end-to-end. We derive its analytical solution and provide its theoretical interpolation error bound. Extensive experiments indicate its merits over both ODE-RNN and cubic spline interpolation.

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Jing Shi, Jing Bi, Yingru Liu, Chenliang Xu. 2020-10-03. Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences. https://arxiv.org/abs/2010.01381

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