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Geometric Deep Learning: Going beyond Euclidean data
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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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Metadata
- Authors
- Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P.
- Affiliations
- Tel Aviv University (IL)
- Courant Institute of Mathematical Sciences (US)
- Meta (United Kingdom) (GB)
- Meta (United States) (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Signal Processing Magazine
- Published
- 2017-07
- Volume
- 34
- Issue
- 4
- Pages
- 18-42
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2558748708
- Cited by
- 3,661
- References
- 137
- Citations by year
- 2026: 350 · 2025: 412 · 2024: 388 · 2023: 428 · 2022: 462 · 2021: 567 · 2020: 493 · 2019: 353 · 2018: 181 · 2017: 25
- FWCI
- 169.40
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- geometric deep learning, non-Euclidean data, graph neural networks, manifold learning, structured data, deep neural network
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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