Bridging Geometry and Deep Learning: Key Developments in SPD and Grassmann Networks | HackerNoon
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from Hackernoon2 months ago
This paper introduces FC and convolutional layers tailored for symmetric positive definite (SPD) neural networks, successfully applying MLR to symmetric positive semi-definite (SPSD) manifolds.
Hackernoonhttps://hackernoon.com/bridging-geometry-and-deep-learning-key-developments-in-spd-and-grassmann-networks
We demonstrate an innovative backpropagation technique using the Grassmann logarithmic map from a projector perspective, which enhances the performance of generalized convolutional networks.
Hackernoonhttps://hackernoon.com/bridging-geometry-and-deep-learning-key-developments-in-spd-and-grassmann-networks
Our experimental results affirm the effectiveness of extending Graph Convolutional Networks to Grassmann geometry, particularly excelling in tasks such as human action recognition and node classification.
Hackernoonhttps://hackernoon.com/bridging-geometry-and-deep-learning-key-developments-in-spd-and-grassmann-networks
Read at Hackernoon
#neural-networks #grassmann-geometry #human-action-recognition #node-classification #backpropagation-techniques
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