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Title: Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

Authors:
; ORCiD logo; ; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1637558
Grant/Contract Number:  
de-sc0019453
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Neural Networks
Additional Journal Information:
Journal Name: Neural Networks Journal Volume: 130 Journal Issue: C; Journal ID: ISSN 0893-6080
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Jin, Pengzhan, Lu, Lu, Tang, Yifa, and Karniadakis, George Em. Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. United States: N. p., 2020. Web. https://doi.org/10.1016/j.neunet.2020.06.024.
Jin, Pengzhan, Lu, Lu, Tang, Yifa, & Karniadakis, George Em. Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. United States. https://doi.org/10.1016/j.neunet.2020.06.024
Jin, Pengzhan, Lu, Lu, Tang, Yifa, and Karniadakis, George Em. Thu . "Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness". United States. https://doi.org/10.1016/j.neunet.2020.06.024.
@article{osti_1637558,
title = {Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness},
author = {Jin, Pengzhan and Lu, Lu and Tang, Yifa and Karniadakis, George Em},
abstractNote = {},
doi = {10.1016/j.neunet.2020.06.024},
journal = {Neural Networks},
number = C,
volume = 130,
place = {United States},
year = {2020},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.neunet.2020.06.024

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Works referenced in this record:

Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
journal, January 2018


Approximation by superpositions of a sigmoidal function
journal, December 1989

  • Cybenko, G.
  • Mathematics of Control, Signals, and Systems, Vol. 2, Issue 4
  • DOI: 10.1007/BF02551274

Balls in Rk do not cut all subsets of k + 2 points
journal, March 1979


Multilayer feedforward networks are universal approximators
journal, January 1989


Gradient-based learning applied to document recognition
journal, January 1998

  • Lecun, Y.; Bottou, L.; Bengio, Y.
  • Proceedings of the IEEE, Vol. 86, Issue 11
  • DOI: 10.1109/5.726791

On the information bottleneck theory of deep learning
journal, December 2019

  • Saxe, Andrew M.; Bansal, Yamini; Dapello, Joel
  • Journal of Statistical Mechanics: Theory and Experiment, Vol. 2019, Issue 12
  • DOI: 10.1088/1742-5468/ab3985

Mastering the game of Go with deep neural networks and tree search
journal, January 2016

  • Silver, David; Huang, Aja; Maddison, Chris J.
  • Nature, Vol. 529, Issue 7587
  • DOI: 10.1038/nature16961

Robust Large Margin Deep Neural Networks
journal, August 2017

  • Sokolic, Jure; Giryes, Raja; Sapiro, Guillermo
  • IEEE Transactions on Signal Processing, Vol. 65, Issue 16
  • DOI: 10.1109/TSP.2017.2708039