Semantic Scholar Open Access 2016 3744 sitasi

Geometric Deep Learning: Going beyond Euclidean data

M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst

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Abstrak

Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them.

Topik & Kata Kunci

Computer Science

Penulis (5)

M. Bronstein

Joan Bruna

Yann LeCun

Arthur Szlam

P. Vandergheynst

Format Sitasi

APA MLA BibTeX

Bronstein, M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P. (2016). Geometric Deep Learning: Going beyond Euclidean data. https://doi.org/10.1109/MSP.2017.2693418

Akses Cepat

Lihat di Sumber doi.org/10.1109/MSP.2017.2693418

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 3744×
Sumber Database: Semantic Scholar
DOI: 10.1109/MSP.2017.2693418
Akses: Open Access ✓