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Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks.
Density estimation for statistics and data analysis
Bernard W. Silverman · 1986
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, and Lawrence D Jackel · 1990
Earlier work this paper cites.
Variable location and scale kernel density estimation
MC Jones, IJ McKay, and T-C Hu · 1994
Earlier work this paper cites.
Simplifying neural nets by discovering flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1995
Earlier work this paper cites.
Numerical Methods for the Solution of Ill-Posed Problems
Andrey Nikolayevich Tikhonov, A. Goncharsky, V. V. Stepanov, and Anatolij Grigorevic Yagola · 1995
Earlier work this paper cites.
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Geometric integration theory
Steven G. Krantz and Harold R. Parks · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
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Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
Robustness and generalization
Huan Xu and Shie Mannor · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina · 2017
Earlier work this paper cites.
Sharp minima can generalize for deep nets
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
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Three factors influencing minima in sgd
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On large-batch training for deep learning: Generalization gap and sharp minima
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Uniform convergence may be unable to explain generalization in deep learning
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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A reparameterization-invariant flatness measure for deep neural networks
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A scale invariant flatness measure for deep network minima
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Empirical analysis of the hessian of over-parametrized neural networks
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Data-dependent pac-bayes priors via differential privacy
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Averaging weights leads to wider optima and better generalization
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Hessian-based analysis of large batch training and robustness to adversaries
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Benign overfitting in linear regression
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