Deep Learning with Functional Inputs
- URL: http://arxiv.org/abs/2006.09590v1
- Date: Wed, 17 Jun 2020 01:23:00 GMT
- Title: Deep Learning with Functional Inputs
- Authors: Barinder Thind, Kevin Multani, Jiguo Cao
- Abstract summary: We present a methodology for integrating functional data into feed-forward neural networks.
A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process.
The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying functional weights.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We present a methodology for integrating functional data into deep densely
connected feed-forward neural networks. The model is defined for scalar
responses with multiple functional and scalar covariates. A by-product of the
method is a set of dynamic functional weights that can be visualized during the
optimization process. This visualization leads to greater interpretability of
the relationship between the covariates and the response relative to
conventional neural networks. The model is shown to perform well in a number of
contexts including prediction of new data and recovery of the true underlying
functional weights; these results were confirmed through real applications and
simulation studies. A forthcoming R package is developed on top of a popular
deep learning library (Keras) allowing for general use of the approach.
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