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We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning.
Positive definite functions on spheres
I.J. Schoenberg et al · 1942
Earlier work this paper cites.
Distributional structure
Z.S. Harris · 1954
Earlier work this paper cites.
Some connections between nonuniform and uniform complexity classes
R.M. Karp and R.J. Lipton · 1980
Earlier work this paper cites.
Theory of reproducing kernels and its applications
S. Saitoh · 1988
Earlier work this paper cites.
What size net gives valid generalization?
E.B. Baum and D. Haussler · 1989
Earlier work this paper cites.
Cryptographic limitations on learning Boolean formulae and finite automata
M. Kearns and L.G. Valiant · 1989
Earlier work this paper cites.
Universal approximation bounds for superposition of a sigmoidal function
A.R. Barron · 1993
Earlier work this paper cites.
Computation with infinite neural networks
C.K.I. Williams · 1997
Earlier work this paper cites.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
P.L. Bartlett · 1998
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.
Prior knowledge in support vector kernels
B. Schölkopf, P. Simard, A. Smola, and V. Vapnik · 1998
Earlier work this paper cites.
Neural Network Learning: Theoretical Foundations
M. Anthony and P. Bartlet · 1999
Earlier work this paper cites.
Rademacher and Gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
Earlier work this paper cites.
The pyramid match kernel: Discriminative classification with sets of image features
K. Grauman and T. Darrell · 2005
Earlier work this paper cites.
Cryptographic hardness for learning intersections of halfspaces
A.R. Klivans and A.A. Sherstov · 2006
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
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun · 2009
Earlier work this paper cites.
A high-throughput screening approach to discovering good forms of biologically inspired visual representation
N. Pinto, D. Doukhan, J.J. DiCarlo, and D.D. Cox · 2009
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
A. Rahimi and B. Recht · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Object recognition with hierarchical kernel descriptors
L. Bo, K. Lai, X. Ren, and D. Fox · 2011
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Beyond simple features: A large-scale feature search approach to unconstrained face recognition
D. Cox and N. Pinto · 2011
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On random weights and unsupervised feature learning
A. Saxe, P.W. Koh, Z. Chen, M. Bhand, B. Suresh, and A.Y. Ng · 2011
Convolutional kernel networks
J. Mairal, P. Koniusz, Z. Harchaoui, and Cordelia Schmid · 2014
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Analysis of boolean functions
R. O’Donnell · 2014
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Provable methods for training neural networks with sparse connectivity
H. Sedghi and A. Anandkumar · 2014
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Understanding Machine Learning: From Theory to Algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q.V. Le · 2014
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Random feature maps for dot product kernels
P. Kar and H. Karnick · 2012
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Imagenet classification with deep convolutional neural networks
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Bayesian learning for neural networks , volume 118
R.M. Neal · 2012
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An evaluation of the invariance properties of a biologically-inspired system for unconstrained face recognition
N. Pinto and D. Cox · 2012
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Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean · 2013
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Deep convolutional networks are hierarchical kernel machines
F. Anselmi, L. Rosasco, C. Tan, and T. Poggio · 2015
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On the equivalence between kernel quadrature rules and random feature expansions
F. Bach · 2015
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The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. Ben Arous, and Y. LeCun · 2015
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Deep neural networks with random gaussian weights: A universal classification strategy?
R. Giryes, G. Sapiro, and A.M. Bronstein · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
M. Hardt, B. Recht, and Y. Singer · 2015
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Steps toward deep kernel methods from infinite neural networks
T. Hazan and T. Jaakkola · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Spherical random features for polynomial kernels
J. Pennington, F. Yu, and S. Kumar · 2015
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On the quality of the initial basin in overspecified neural networks
I. Safran and O. Shamir · 2015
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Complexity theoretic limitations on learning halfspaces
A. Daniely · 2016
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Complexity theoretic limitations on learning DNFs
A. Daniely and S. Shalev-Shwartz · 2016
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