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Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision.
Scale-space and edge detection using anisotropic diffusion
P. Perona and J. Malik · 1990
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
A general framework for low level vision
N. Sochen, R. Kimmel, and R. Malladi · 1998
Earlier work this paper cites.
Normalized cuts and image segmentation
J. Shi and J. Malik · 2000
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
A. Y. Ng, M. I. Jordan, and Y. Weiss · 2002
Earlier work this paper cites.
Graph-based representations and techniques for image processing and image analysis
A. Sanfeliu et al · 2002
Earlier work this paper cites.
Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
X. Zhu, Z. Ghahramani, J. Lafferty, et al · 2003
Earlier work this paper cites.
SCAPE: shape completion and animation of people
D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, and J. Davis · 2005
Earlier work this paper cites.
A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
Earlier work this paper cites.
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M. Belkin, P. Niyogi, and V. Sindhwani · 2006
Earlier work this paper cites.
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R. R. Coifman and M. Maggioni · 2006
Earlier work this paper cites.
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I. S. Dhillon, Y. Guan, and B. Kulis · 2007
Earlier work this paper cites.
Numerical geometry of non-rigid shapes
A. M. Bronstein, M. M. Bronstein, and R. Kimmel · 2008
Earlier work this paper cites.
Collective classification in network data
P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad · 2008
Earlier work this paper cites.
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F. Zhang and E. R. Hancock · 2008
Earlier work this paper cites.
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F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Earlier work this paper cites.
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Y. Weiss, A. Torralba, and R. Fergus · 2009
Earlier work this paper cites.
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M. Gavish, B. Nadler, and R. R. Coifman · 2010
Earlier work this paper cites.
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F. Tombari, S. Salti, and L. Di Stefano · 2010
Earlier work this paper cites.
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D. K. Hammond, P. Vandergheynst, and R. Gribonval · 2011
Earlier work this paper cites.
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V. Kim, Y. Lipman, and T. Funkhouser · 2011
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I. Kokkinos, M. Bronstein, R. Litman, and A. Bronstein · 2012
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O. Lézoray and L. Grady · 2012
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J. Weston, F. Ratle, H. Mobahi, and R. Collobert · 2012
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Learning of structured graph dictionaries
X. Zhang, X. Dong, and P. Frossard · 2012
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
Cited alongside, same era.
Geodesic convolutional neural networks on Riemannian manifolds
J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst · 2015
Later among the works it cites.
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N. Shahid, V. Kalofolias, X. Bresson, M. M. Bronstein, and P. Vandergheynst · 2015
Later among the works it cites.
A class of Laplacian multiwavelets bases for high-dimensional data
N. Sharon and Y. Shkolnisky · 2015
Later among the works it cites.
Multi-view convolutional neural networks for 3D shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
Later among the works it cites.
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J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei · 2015
Later among the works it cites.
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Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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V. Kalofolias, X. Bresson, M. M. Bronstein, and P. Vandergheynst · 2014
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