Fetching the paper…
Reading the bibliography…
In this work, we build a generic architecture of Convolutional Neural Networks to discover empirical properties of neural networks.
Location-adaptive density estimation and nearest-neighbor distance
P. Burman and D. Nolan · 1992
Earlier work this paper cites.
Support-vector networks
C. Cortes and V. Vapnik · 1995
Earlier work this paper cites.
A wavelet tour of signal processing
S. Mallat · 1999
Earlier work this paper cites.
Methods of pattern recognition
V. N. Vapnik · 2000
Earlier work this paper cites.
Estimating local intrinsic dimension with k-nearest neighbor graphs
J. A. Costa, A. Girotra, and A. Hero · 2005
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, Y. Lecun, et al · 2009
Earlier work this paper cites.
Learning where to attend with deep architectures for image tracking
M. Denil, L. Bazzani, H. Larochelle, and N. de Freitas · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Group invariant scattering
S. Mallat · 2012
Earlier work this paper cites.
Intrinsic dimension identification via graph-theoretic methods
M. Brito, A. Quiroz, and J. E. Yukich · 2013
Earlier work this paper cites.
Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
Earlier work this paper cites.
Learning stable group invariant representations with convolutional networks
J. Bruna, A. Szlam, and Y. LeCun · 2013
Cited alongside, same era.
S. Mallat and I. Waldspurger · 2013
Cited alongside, same era.
Rotation, scaling and deformation invariant scattering for texture discrimination
L. Sifre and S. Mallat · 2013
Cited alongside, same era.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Later among the works it cites.
Understanding image representations by measuring their equivariance and equivalence
K. Lenc and A. Vedaldi · 2015
Later among the works it cites.
Exploring how deep neural networks form phonemic categories
T. Nagamine, M. L. Seltzer, and N. Mesgarani · 2015
Later among the works it cites.
Deep roto-translation scattering for object classification
E. Oyallon and S. Mallat · 2015
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Understanding deep features with computer-generated imagery
M. Aubry and B. C. Russell · 2015
Cited alongside, same era.
Manitest: Are classifiers really invariant?
A. Fawzi and P. Frossard · 2015
Cited alongside, same era.
Deep neural networks with random gaussian weights: A universal classification strategy?
R. Giryes, G. Sapiro, and A. M. Bronstein · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
I. Loshchilov and F. Hutter · 2016
Later among the works it cites.
Understanding deep convolutional networks
S. Mallat · 2016
Later among the works it cites.
Robust large margin deep neural networks
J. Sokolic, R. Giryes, G. Sapiro, and M. R. Rodrigues · 2016
Later among the works it cites.
S. Zagoruyko and N. Komodakis · 2016
Later among the works it cites.
Residual networks of residual networks: Multilevel residual networks
K. Zhang, M. Sun, T. X. Han, X. Yuan, L. Guo, and T. Liu · 2016
Later among the works it cites.