Geometric deep learning

By Jonathan Masci, Emanuele Rodolà, Davide Boscaini, Michael M. Bronstein, Hao Li. 2016

102 citations recorded by the source corpus.

Subfields: Computational Mechanics, Control and Systems Engineering, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. M. Ovsjanikov
2. Niloy J. Mitra
3. Paul Guerrero
4. Ben Glocker
5. Daniel Rueckert
6. Martin Rajchl
7. Yanir Kleiman
8. Sarah Parisot

In Control and Systems Engineering

1. Ben Glocker
2. Niloy J. Mitra

In Industrial and Manufacturing Engineering

1. M. Ovsjanikov
2. Niloy J. Mitra

Citations in the corpus

• PCPNet Learning Local Shape Properties from Raw Point Clouds (2017, methodology): “In the recent years, several methods have been proposed for analyzing and processing 3D shapes by building on the success of machine learning methods, and especially those based on deep learning (see, for example, recent overviews in [MRB ∗ 16, BBL ∗ 17]).”
• Metric learning with spectral graph convolutions on brain connectivity networks (2018, background): “This research topic has recently attracted a lot of attention and relevant work is focusing on the challenging problem of defining a local neighbourhood structure, which is required for convolution operations (Henaff et al., 2015; Niepert et al., 2016; Masci et al., 2016).”