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Found 4 papers, 2 papers with code
Activation Landscapes as a Topological Summary of Neural Network Performance
We use topological data analysis (TDA) to study how data transforms as it passes through successive layers of a deep neural network (DNN).
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
In this paper, we present a novel higher order Volterra convolutional neural network (VolterraNet) for data defined as samples of functions on Riemannian homogeneous spaces.
MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA
To this end, we present a provably convergent recursive computation of the wFM of the given data, where the weights makeup the convolution mask, to be learned.
ManifoldNet: A Deep Network Framework for Manifold-valued Data
Thus, there is need to generalize the deep neural networks to cope with input data that reside on curved manifolds where vector space operations are not naturally admissible.
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