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Found 6 papers, 0 papers with code
Multiscale Hodge Scattering Networks for Data Analysis
Our construction is based on multiscale basis dictionaries on simplicial complexes, i. e., the $\kappa$-GHWT and $\kappa$-HGLET, which we recently developed for simplices of dimension $\kappa \in \mathbb{N}$ in a given simplicial complex by generalizing the node-based Generalized Haar-Walsh Transform (GHWT) and Hierarchical Graph Laplacian Eigen Transform (HGLET).
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders
Autoencoding is a popular method in representation learning.
Computational Analysis of Deformable Manifolds: from Geometric Modelling to Deep Learning
More specifically, we will study techniques for representing manifolds and signals supported on them through a variety of mathematical tools including, but not limited to, computational differential geometry, variational PDE modeling, and deep learning.
Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE
We also successfully detect a level of geometric disentanglement in mesh convolutional autoencoders that encode xyz-coordinates directly by registering its latent space to that of CFAN-VAE.
Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit
In this paper, we propose a variational model to align the Laplace-Beltrami (LB) eigensytems of two non-isometric genus zero shapes via conformal deformations.
Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds
PTC allows for the construction of compactly supported filters and is also robust to manifold deformations.
