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As deep neural networks (DNNs) achieve tremendous success across many application domains, researchers tried to explore in many aspects on why they generalize well.
Stiffness: A new perspective on generalization in neural networks, 2019
Stanislaw Jastrzebski Srini Narayanan Stanislav Fort, Paweł Krzysztof Nowak · 1901
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The necessary and sufficient conditions for consistency of the method of empirical risk
Vladimir N Vapnik and A Ja Chervonenkis · 1991
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Adadelta: An adaptive learning rate method, 2012
Matthew D. Zeiler · 2012
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Agenerating sequences with recurrent neural networks, 2013
Alex Graves · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Benjamin Recht, and Yoram Singer · 2015
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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 · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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High-dimensional dynamics of generalization error in neural networks
Madhu S Advani and Andrew M Saxe · 2017
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Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
Cited alongside, same era.
Generalization in deep learning
Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Stronger generalization bounds for deep nets via a compression approach, 2018
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
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Sensitivity and generalization in neural networks: An empirical study
Roman Novak, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Gintare Karolina Dziugaite and Daniel M Roy · 2017
Cited alongside, same era.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2017
Cited alongside, same era.
Tom Rainforth, Adam R Kosiorek, Tuan Anh Le, Chris J Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh · 2018
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Generalization error in deep learning, 2019
Daniel Jakubovitz, Raja Giryes, and Miguel RD Rodrigues · 2019
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