geometric deep learning on graphs and manifolds

Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti1∗ Davide Boscaini1∗ Jonathan Masci1,4 Emanuele Rodola`1 Jan Svoboda1 Michael M. Bronstein1,2,3 1USI Lugano 2Tel Aviv University 3Intel Perceptual Computing 4Nnaisense Abstract Deep learning has achieved a remarkable performance Related works Deep learning on graphs. The earliest attempts to gener- We collect workshops, tutorials, publications and code, that several differet researchers has produced in the last years. The success of deep learning methods in many fields has recently provoked a Equal contribution keen interest in geometric deep learning [10] attempting to generalize such methods to non-Euclidean structured data. The purpose of this minitutorial is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications for this class of problems, as well as key difficulties and future research directions.
It seeks to apply traditional Convolutional Neural Networks to 3D objects, graphs and manifolds. Michael … It seeks to apply traditional Convolutional Neural Networks to 3D objects, graphs and manifolds.

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.

Geometric Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data April 16, 2018 - 04:00 - April 16, 2018 - 05:00 Michael Bronstein, Università della Svizzera italiana (Switzerland), Tel Aviv University (Israel
The purpose of the proposed tutorial is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications for this class of problems, as well as key difficulties and future research directions. So far research has mainly focused on developing deep learning methods for Euclidean-structured data, while many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, … In this story I will show you some of geometric deep learning applications, such as: Geometric Deep Learning deals in this sense with the extension of Deep Learning techniques to graph/manifold structured data. Geometric deep learning is a new field of machine learning that can learn from complex data like graphs and multi-dimensional points.


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