• Corpus ID: 233423603

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

@article{Bronstein2021GeometricDL,
  title={Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges},
  author={Michael M. Bronstein and Joan Bruna and Taco Cohen and Petar Velivckovi'c},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.13478}
}
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact feasible with appropriate computational scale. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning, whereby adapted… 
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