17 citations · 32 across the 3 of their papers we have counts for
4 papers · 1 filter
Learning to recover orientations from projections in single-particle cryo-EM
Jelena Banjac, Laurène Donati, Michaël Defferrard
A major challenge in single-particle cryo-electron microscopy (cryo-EM) is that the orientations adopted by the 3D particles prior to imaging are unknown; yet, this knowledge is es…
DeepSphere: a graph-based spherical CNN
Michaël Defferrard, Martino Milani, Frédérick Gusset +1
Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representati…
Simplicial Neural Networks
Stefania Ebli, Michaël Defferrard, Gard Spreemann
We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes. These are na…
DeepSphere: towards an equivariant graph-based spherical CNN
Michaël Defferrard, Nathanaël Perraudin, Tomasz Kacprzak +1
Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover…