Fetching the paper…
Reading the bibliography…
Contemporary deep neural networks exhibit impressive results on practical problems.
Some new results on neural network approximation
Kurt Hornik · 1993
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 1995
Earlier work this paper cites.
Computing with infinite networks
Christopher KI Williams · 1997
Earlier work this paper cites.
Advances in kernel methods: support vector learning
Bernhard Schölkopf, Christopher JC Burges, and Alexander J Smola · 1999
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
Convex neural networks
Yoshua Bengio, Nicolas L Roux, Pascal Vincent, Olivier Delalleau, and Patrice Marcotte · 2005
Earlier work this paper cites.
Online passive-aggressive algorithms
Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, and Yoram Singer · 2006
Earlier work this paper cites.
Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
Sayan Mukherjee, Partha Niyogi, Tomaso Poggio, and Ryan Rifkin · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
Carl Edward Rasmussen · 2006
Cited alongside, same era.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
Cited alongside, same era.
Exponentiated gradient algorithms for conditional random fields and max-margin markov networks
M. Collins, A. Globerson, T. Koo, X. Carreras, and P.L. Bartlett · 2008
Cited alongside, same era.
Learning deep architectures for ai
Yoshua Bengio · 2009
Cited alongside, same era.
Kernel methods for deep learning
Youngmin Cho and Lawrence K Saul · 2009
Cited alongside, same era.
Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2009
Cited alongside, same era.
Arccosine kernels: Acoustic modeling with infinite neural networks
Chih-Chieh Cheng and Brian Kingsbury · 2011
Later among the works it cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Later among the works it cites.
A stochastic gradient method with an exponential convergence _rate for finite training sets
Nicolas L Roux, Mark Schmidt, and Francis R Bach · 2012
Later among the works it cites.
Fastfood–approximating kernel expansions in loglinear time
Quoc Le, Tamás Sarlós, and Alex Smola · 2013
Later among the works it cites.
Learning with marginalized corrupted features
Laurens Maaten, Minmin Chen, Stephen Tyree, and Kilian Q Weinberger · 2013
Later among the works it cites.
Dropout training as adaptive regularization
Stefan Wager, Sida Wang, and Percy S Liang · 2013
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Large-margin classification in infinite neural networks
Youngmin Cho and Lawrence K Saul · 2010
Cited alongside, same era.
Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
Cited alongside, same era.
Later among the works it cites.
Kernel methods match deep neural networks on timit
Po-Sen Huang, Haim Avron, Tara N Sainath, Vikas Sindhwani, and Bhuvana Ramabhadran · 2014
Later among the works it cites.
How to scale up kernel methods to be as good as deep neural nets
Zhiyun Lu, Avner May, Kuan Liu, Alireza Bagheri Garakani, Dong Guo, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, et al · 2014
Later among the works it cites.