Imperial College London

ProfessorMichaelBronstein

Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition
 
 
 
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Contact

 

m.bronstein Website

 
 
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Location

 

569Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Monti:2017:10.1109/CVPR.2017.576,
author = {Monti, F and Boscaini, D and Masci, J and Rodolà, E and Svoboda, J and Bronstein, MM},
doi = {10.1109/CVPR.2017.576},
pages = {5425--5434},
title = {Geometric deep learning on graphs and manifolds using mixture model CNNs},
url = {http://dx.doi.org/10.1109/CVPR.2017.576},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - © 2017 IEEE. Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches.
AU - Monti,F
AU - Boscaini,D
AU - Masci,J
AU - Rodolà,E
AU - Svoboda,J
AU - Bronstein,MM
DO - 10.1109/CVPR.2017.576
EP - 5434
PY - 2017///
SP - 5425
TI - Geometric deep learning on graphs and manifolds using mixture model CNNs
UR - http://dx.doi.org/10.1109/CVPR.2017.576
ER -