17 citations · 32 across the 3 of their papers we have counts for
6 papers
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…
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Nathanaël Perraudin, Michaël Defferrard, Tomasz Kacprzak +1
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been d…
Learning to Recognize Musical Genre from Audio
Michaël Defferrard, Sharada P. Mohanty, Sean F. Carroll +1
We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics abou…