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1
Intro
2
DEEP LEARNING ON REGULAR GRIDS
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TOWARDS NON-EUCLIDEAN GEOMETRIES
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PHYSICS-BASED GEOMETRIC LEARNING
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GEOMETRIC STABILITY IN EUCLIDEAN DOMAINS
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CONVOLUTIONAL NEURAL NETWORKS
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NON-EUCLIDEAN GEOMETRIC STABILITY
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DEFORMATIONS AND METRICS
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DIFFUSION AND METRIC STABILITY
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LINEAR STABLE GENERATORS
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LAPLACIAN INTERPRETATION
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SURFACE REPRESENTATIONS
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PARTICLE PHYSICS WITH GRAPH NEURAL NETWORKS
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ICECUBE NEUTRINO DETECTION
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INVERSE PROBLEMS ON GRAPHS
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DABL-DRIVEN COMMUNITY DETECTION
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REACHING DETECTION THRESHOLD ON SEM
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GRAPH NEURAL NETWORKS ON GRAPH HIERARCHES
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COMPUTATIONAL-TO-STATISTICAL GAPS
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SUPERVISED COMMUNITY DETECTION
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CURRENT AND OPEN PROBLEMS
Description:
Explore geometric deep learning applied to inverse problems on graphs in this 33-minute conference talk from the 2018 Physics Next workshop. Delve into topics like non-Euclidean geometries, physics-based geometric learning, and graph neural networks. Learn how these techniques are applied to particle physics, neutrino detection, and community detection. Discover the latest developments in computational-to-statistical gaps and supervised community detection, and gain insights into current challenges and open problems in the field.

Inverse Problems on Graphs with Geometric Deep Learning

APS Physics
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