Fetching the paper…
Reading the bibliography…
The success of deep convolutional architectures is often attributed in part to their ability to learn multiscale and invariant representations of natural signals.
Positive definite functions on spheres
I. J. Schoenberg · 1942
Earlier work this paper cites.
Vector Measures
J. Diestel and J. J. Uhl · 1977
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Harmonic Analysis: Real-variable Methods, Orthogonality, and Oscillatory Integrals
E. M. Stein · 1993
Earlier work this paper cites.
Integral transforms, reproducing kernels and their applications , volume 369
S. Saitoh · 1997
Earlier work this paper cites.
Support Vector Learning
B. Schölkopf · 1997
Earlier work this paper cites.
Nonlinear component analysis as a kernel eigenvalue problem
B. Schölkopf, A. Smola, and K.-R. Müller · 1998
Earlier work this paper cites.
Sparse greedy matrix approximation for machine learning
A. J. Smola and B. Schölkopf · 2000
Earlier work this paper cites.
Efficient SVM training using low-rank kernel representations
S. Fine and K. Scheinberg · 2001
Earlier work this paper cites.
Learning with kernels: support vector machines, regularization, optimization, and beyond
B. Schölkopf and A. J. Smola · 2001
Earlier work this paper cites.
Using the Nyström method to speed up kernel machines
C. Williams and M. Seeger · 2001
Earlier work this paper cites.
Statistics of natural image categories
A. Torralba and A. Oliva · 2003
Earlier work this paper cites.
Theory of classification: A survey of some recent advances
S. Boucheron, O. Bousquet, and G. Lugosi · 2005
Earlier work this paper cites.
Local geometry of deformable templates
A. Trouvé and L. Younes · 2005
Earlier work this paper cites.
Towards a coherent statistical framework for dense deformable template estimation
S. Allassonnière, Y. Amit, and A. Trouvé · 2007
Earlier work this paper cites.
Invariant kernel functions for pattern analysis and machine learning
B. Haasdonk and H. Burkhardt · 2007
Earlier work this paper cites.
Training invariant support vector machines using selective sampling
G. Loosli, S. Canu, and L. Bottou · 2007
Earlier work this paper cites.
Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
Earlier work this paper cites.
Kernel methods for deep learning
Y. Cho and L. K. Saul · 2009
Cited alongside, same era.
Object recognition with hierarchical kernel descriptors
L. Bo, K. Lai, X. Ren, and D. Fox · 2011
Cited alongside, same era.
Kernel analysis of deep networks
G. Montavon, M. L. Braun, and K.-R. Müller · 2011
Cited alongside, same era.
Group invariant scattering
S. Mallat · 2012
Cited alongside, same era.
Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
Cited alongside, same era.
Learning stable group invariant representations with convolutional networks
J. Bruna, A. Szlam, and Y. LeCun · 2013
Cited alongside, same era.
A course in abstract harmonic analysis
G. B. Folland · 2016
Later among the works it cites.
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
J. Mairal · 2016
Later among the works it cites.
Learning with hierarchical gaussian kernels
I. Steinwart, P. Thomann, and N. Schmid · 2016
Later among the works it cites.
ℓ 1 \ell_{1} -regularized neural networks are improperly learnable in polynomial time
Y. Zhang, J. D. Lee, and M. I. Jordan · 2016
Later among the works it cites.
On the equivalence between kernel quadrature rules and random feature expansions
F. Bach · 2017
Closest in time.
Spectrally-normalized margin bounds for neural networks
P. Bartlett, D. J. Foster, and M. Telgarsky · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rotation, scaling and deformation invariant scattering for texture discrimination
L. Sifre and S. Mallat · 2013
Cited alongside, same era.
Convolutional kernel networks
J. Mairal, P. Koniusz, Z. Harchaoui, and C. Schmid · 2014
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Cited alongside, same era.
Deep convolutional networks are hierarchical kernel machines
F. Anselmi, L. Rosasco, C. Tan, and T. Poggio · 2015
Cited alongside, same era.
Closest in time.
Invariance and stability of deep convolutional representations
A. Bietti and J. Mairal · 2017
Closest in time.
Parseval networks: Improving robustness to adversarial examples
M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier · 2017
Closest in time.
Random features for compositional kernels
A. Daniely, R. Frostig, V. Gupta, and Y. Singer · 2017
Closest in time.
Fisher-Rao metric, geometry, and complexity of neural networks
T. Liang, T. Poggio, A. Rakhlin, and J. Stokes · 2017
Closest in time.
Kernel mean embedding of distributions: A review and beyond
K. Muandet, K. Fukumizu, B. Sriperumbudur, B. Schölkopf, et al · 2017
Closest in time.
Exploring generalization in deep learning
B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro · 2017
Closest in time.
Local group invariant representations via orbit embeddings
A. Raj, A. Kumar, Y. Mroueh, T. Fletcher, and B. Schoelkopf · 2017
Closest in time.
Stronger generalization bounds for deep nets via a compression approach
S. Arora, R. Ge, B. Neyshabur, and Y. Zhang · 2018
Closest in time.
On regularization and robustness of deep neural networks
A. Bietti, G. Mialon, and J. Mairal · 2018
Closest in time.
On the generalization of equivariance and convolution in neural networks to the action of compact groups
R. Kondor and S. Trivedi · 2018
Closest in time.
A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks
B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro · 2018
Closest in time.
A mathematical theory of deep convolutional neural networks for feature extraction
T. Wiatowski and H. Bölcskei · 2018
Closest in time.