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Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
The effects of adding noise during backpropagation training on a generalization performance
Guozhong An · 1996
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Fault injection attacks on cryptographic devices: Theory, practice, and countermeasures
Alessandro Barenghi, Luca Breveglieri, Israel Koren, and David Naccache · 2012
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Robustness and generalization
Huan Xu and Shie Mannor · 2012
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Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
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Drammer: Deterministic rowhammer attacks on mobile platforms
Victor Van Der Veen, Yanick Fratantonio, Martina Lindorfer, Daniel Gruss, Clémentine Maurice, Giovanni Vigna, Herbert Bos, Kaveh Razavi, and Cristiano Giuffrida · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Gintare Karolina Dziugaite and Daniel M. Roy · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
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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 · 2017
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Fault injection attack on deep neural network
Yannan Liu, Lingxiao Wei, Bo Luo, and Qiang Xu · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Approximation and estimation for high-dimensional deep learning networks
Andrew R Barron and Jason M Klusowski · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
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Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Rademacher complexity for adversarially robust generalization
Dong Yin, Ramchandran Kannan, and Peter Bartlett · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Fault sneaking attack: A stealthy framework for misleading deep neural networks
Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, and Xue Lin · 2019
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Feature-robustness, flatness and generalization error for deep neural networks
Henning Petzka, Linara Adilova, Michael Kamp, and Cristian Sminchisescu · 2020
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Towards certificated model robustness against weight perturbations
Tsui-Wei Weng, Pu Zhao, Sijia Liu, Pin-Yu Chen, Xue Lin, and Luca Daniel · 2020
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Size-independent sample complexity of neural networks, 2019
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2019
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Scalable verified training for provably robust image classification
Sven Gowal, Krishnamurthy Dj Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2019
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Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J Zico Kolter · 2019
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Global capacity measures for deep relu networks via path sampling
Ryan Theisen, Jason M Klusowski, Huan Wang, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
Cited alongside, same era.
Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Bridging mode connectivity in loss landscapes and adversarial robustness
Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, and Xue Lin · 2020
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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Diametrical risk minimization: theory and computations
Matthew D Norton and Johannes O Royset · 2021
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Bit error robustness for energy-efficient dnn accelerators
David Stutz, Nandhini Chandramoorthy, Matthias Hein, and Bernt Schiele · 2021
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