Search Results for author:
Found 4 papers, 3 papers with code
Equivariant Manifold Flows
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences.
Neural Manifold Ordinary Differential Equations
To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces.
Differentiating through the Fréchet Mean
Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold.
Adversarial Example Decomposition
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
