Fetching the paper…
Reading the bibliography…
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrices, times a certain correction factor.
Remarques sur un résultat non publié de b. maurey
Gilles Pisier · 1980
Earlier work this paper cites.
Estimation of Dependences Based on Empirical Data
Vladimir N. Vapnik · 1982
Earlier work this paper cites.
A training algorithm for optimal margin classifiers
Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik · 1992
Earlier work this paper cites.
Support-vector networks
Corinna Cortes and Vladimir N. Vapnik · 1995
Earlier work this paper cites.
The Nature of Statistical Learning Theory
Vladimir N. Vapnik · 1995
Earlier work this paper cites.
For valid generalization the size of the weights is more important than the size of the network
Peter L. Bartlett · 1996
Earlier work this paper cites.
Boosting the margin: A new explanation for the effectiveness of voting methods
Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee · 1997
Earlier work this paper cites.
Structural risk minimization over data-dependent hierarchies
J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony · 1998
Earlier work this paper cites.
Neural Network Learning: Theoretical Foundations
Martin Anthony and Peter L. Bartlett · 1999
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L. Bartlett and Shahar Mendelson · 2002
Cited alongside, same era.
Covering number bounds of certain regularized linear function classes
Tong Zhang · 2002
Cited alongside, same era.
Statistical analysis of some multi-category large margin classification methods
Tong Zhang · 2004
Cited alongside, same era.
Theory of classification: A survey of some recent advances
Stéphane Boucheron, Olivier Bousquet, and Gabor Lugosi · 2005
Cited alongside, same era.
On the consistency of multiclass classification methods
Ambuj Tewari and Peter L. Bartlett · 2007
Cited alongside, same era.
On the equivalence of weak learnability and linear separability: New relaxations and efficient boosting algorithms
Shai Shalev-Shwartz and Yoram Singer · 2008
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Later among the works it cites.
Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
Later among the works it cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J Foster, and Matus Telgarsky · 2017
Closest in time.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffery Hinton · 2012
Cited alongside, same era.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
Cited alongside, same era.
Behnam Neyshabur · 2017
Closest in time.
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro · 2017
Closest in time.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Closest in time.