Geometric deep learning: going beyond Euclidean data.
IEEE Signal Processing Magazine(2017)
摘要
Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains, such as graphs and manifolds. The purpose of this article is to overview different examples of geometric deep-learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field.
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关键词
Convolution,Computational modeling,Euclidean distance,Machine learning,Convolutional codes,Social network services,Computer architecture
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