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Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers.
- We adapt this approach to the convolutional setting.
Reading the bibliography…
Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers.
Reading the bibliography…
A universal prior for integers and estimation by minimum description length
Jorma Rissanen · 1983
Earlier work this paper cites.
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
A pac analysis of a bayesian estimator
John Shawe-Taylor and Robert C Williamson · 1997
Earlier work this paper cites.
Some pac-bayesian theorems
David A McAllester · 1999
Earlier work this paper cites.
Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Earlier work this paper cites.
Sharp nonasymptotic bounds on the norm of random matrices with independent entries
Afonso S Bandeira, Ramon Van Handel, et al · 2016
Cited alongside, same era.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
Cited alongside, same era.
Robust large margin deep neural networks
Jure Sokolić, Raja Giryes, Guillermo Sapiro, and Miguel RD Rodrigues · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
Cited alongside, same era.
Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
Cited alongside, same era.
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro
Cited in the paper.
Geometry of optimization and implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, and Nathan Srebro
Cited in the paper.
Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
Closest in time.
Data-dependent stability of stochastic gradient descent
Ilja Kuzborskij and Christoph Lampert · 2017
Closest in time.
The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, and Nathan Srebro · 2017
Closest in time.
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
Closest in time.
Feta: A dca pruning algorithm with generalization error guarantees
Konstantinos Pitas, Mike Davies, and Pierre Vandergheynst · 2018
Closest in time.
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