Spatially Embedded Neural Networks: Verifying Coordinate Backpropagation in a Feed Forward MLP

The purpose of this research note is to verify coordinate backpropagation for a spatially embedded feed-forward MLP. The author applies the chain rule to obtain loss-coordinate derivatives for a L-layer, Nl-width MLP embedded in 3D Euclidean Space. Mean-squared error loss is used.

Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.29580692.v2
Primary Topic
Neural Networks and Applications
Type
article
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article

Spatially Embedded Neural Networks: Verifying Coordinate Backpropagation in a Feed Forward MLP

Figshare
Neural Networks and Applications
article

Spatially Embedded Neural Networks: Verifying Coordinate Backpropagation in a Feed Forward MLP

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Abstract

The purpose of this research note is to verify coordinate backpropagation for a spatially embedded feed-forward MLP. The author applies the chain rule to obtain loss-coordinate derivatives for a L-layer, Nl-width MLP embedded in 3D Euclidean Space. Mean-squared error loss is used.

Figshare
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Neural Networks and Applications
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Spatially Embedded Neural Networks: Verifying Coordinate Backpropagation in a Feed Forward MLP · Figshare (2025) | TGRS Research Map | TGRS