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pro vyhledávání: '"Simonovsky, Martin"'
Autor:
Simonovsky, Martin
Le graphe est un concept puissant pour la représentation des relations entre des paires d'entités. Les données ayant une structure de graphes sous-jacente peuvent être trouvées dans de nombreuses disciplines, décrivant des composés chimiques,
Externí odkaz:
http://www.theses.fr/2018PESC1133/document
Autor:
Simonovsky, Martin
A graph is a powerful concept for representation of relations between pairs of entities. Data with underlying graph structure can be found across many disciplines and there is a natural desire for understanding such data better. Deep learning (DL) ha
Externí odkaz:
http://arxiv.org/abs/1901.08296
Autor:
Simonovsky, Martin, Komodakis, Nikos
Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text. Is it possible to
Externí odkaz:
http://arxiv.org/abs/1802.03480
Autor:
Landrieu, Loic, Simonovsky, Martin
We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called s
Externí odkaz:
http://arxiv.org/abs/1711.09869
Autor:
Simonovsky, Martin, Komodakis, Nikos
A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying
Externí odkaz:
http://arxiv.org/abs/1704.02901
Autor:
Simonovsky, Martin, Gutiérrez-Becker, Benjamín, Mateus, Diana, Navab, Nassir, Komodakis, Nikos
Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a g
Externí odkaz:
http://arxiv.org/abs/1609.05396
Autor:
Simonovsky, Martin, Komodakis, Nikos
The focus of our work is speeding up evaluation of deep neural networks in retrieval scenarios, where conventional architectures may spend too much time on negative examples. We propose to replace a monolithic network with our novel cascade of featur
Externí odkaz:
http://arxiv.org/abs/1608.02728
Autor:
Bolo, Kyle, Aroca, Galo Apolo, Pardeshi, Anmol A., Chiang, Michael, Burkemper, Bruce, Xiaobin Xie, Huang, Alex S., Simonovsky, Martin, Xu, Benjamin Y.
Publikováno v:
British Journal of Ophthalmology; May2024, Vol. 108 Issue 5, p702-709, 8p
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Akademický článek
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