Fetching the paper…
Reading the bibliography…
Stability is a key aspect of data analysis.
Spectral Graph Theory
F. R. K. Chung · 1997
Earlier work this paper cites.
Diffusion maps
R. R. Coifman and S. Lafon · 2006
Earlier work this paper cites.
Diffusion wavelets
R. R. Coifman and M. Maggioni · 2006
Earlier work this paper cites.
Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators
B. Nadler, S. Lafon, I. Kevrekidis, and R. R. Coifman · 2006
Earlier work this paper cites.
Social and Economic Networks
M. O. Jackson · 2008
Earlier work this paper cites.
A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching
A. M. Bronstein, M. M. Bronstein, R. Kimmel, M. Mahmoudi, and G. Sapiro · 2010
Earlier work this paper cites.
Multiscale wavelets on trees, graphs and high dimensional data: Theory and applications to semi supervised learning
M. Gavish, B. Nadler, and R. R. Coifman · 2010
Earlier work this paper cites.
Convolutional networks and applications in vision
Y. LeCun, K. Kavukcuoglu, and C. Farabet · 2010
Earlier work this paper cites.
Wavelets on graphs via spectral graph theory
D. K. Hammond, P. Vandergheynst, and R. Gribonval · 2011
Earlier work this paper cites.
Group invariant scattering
S. Mallat · 2012
Earlier work this paper cites.
Learning to discover social circles in Ego networks
J. McAuley and J. Leskovec · 2012
Earlier work this paper cites.
Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
Earlier work this paper cites.
Wavelets on graphs via deep learning
R. Rustamov and L. J. Guibas · 2013
Cited alongside, same era.
Rotation, scaling and deformation invariant scattering for texture discrimination
L. Sifre and S. Mallat · 2013
Cited alongside, same era.
Deep scattering spectrum
J. Andén and S. Mallat · 2014
Cited alongside, same era.
Spectral networks and deep locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
Cited alongside, same era.
Unsupervised deep Haar scattering on graphs
X. Chen, X. Cheng, and S. Mallat · 2014
Cited alongside, same era.
Deep convolutional networks on graph-structured data
M. Henaff, J. Bruna, and Y. LeCun · 2015
Solid harmonic wavelet scattering: Predicting quantum molecular energy from invariant descriptors of 3d electronic densities
M. Eickenberg, G. Exarchakis, M. Hirn, and S. Mallat · 2017
Later among the works it cites.
Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Later among the works it cites.
I. Kostrikov, J. Bruna, D. Panozzo, and D. Zorin · 2017
Later among the works it cites.
Cayleynets: Graph convolutional neural networks with complex rational spectral filters
R. Levie, F. Monti, X. Bresson, and M. M. Bronstein · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Deep roto-translation scattering for object classification
E. Oyallon and S. Mallat · 2015
Cited alongside, same era.
Authorship attribution through function word adjacency networks
S. Segarra, M. Eisen, and A. Ribeiro · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Cited alongside, same era.
Graph frequency analysis of brain signals
W. Huang, L. Goldsberry, N. F. Wymbs, S. T. Grafton, D. S. Bassett, and A. Ribeiro · 2016
Cited alongside, same era.
Geometric deep learning: Going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Cited alongside, same era.
F. Gama, A. G. Marques, G. Leus, and A. Ribeiro · 2018
Closest in time.
Characterizing implicit bias in terms of optimization geometry
Suriya Gunasekar, Jason Lee, Daniel Soudry, and Nathan Srebro · 2018
Closest in time.
Matrix completion via graph signal processing
W. Huang, A. G. Marques, and A. Ribeiro · 2018
Closest in time.
On the margin theory of feedforward neural networks
Colin Wei, Jason D Lee, Qiang Liu, and Tengyu Ma · 2018
Closest in time.
A mathematical theory of deep convolutional neural networks for feature extraction
T. Wiatowski and H. Bölcskei · 2018
Closest in time.
Graph convolutional neural networks via scattering
D. Zou and G. Lerman · 2018
Closest in time.