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It is well known that modern deep neural networks are powerful enough to memorize datasets even when the labels have been randomized.
Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition
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Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2019
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Small relu networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie · 2019
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Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 2020
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Network size and weights size for memorization with two-layers neural networks
Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, and Dan Mikulincer · 2020
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Bithreshold neural network classifier
Vladyslav Kotsovsky, Fedir Geche, and Anatoliy Batyuk · 2020
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Cited alongside, same era.
The role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2018
Cited alongside, same era.
High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
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Shengchao Liu, Dimitris Papailiopoulos, and Dimitris Achlioptas · 2020
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Provable memorization via deep neural networks using sub-linear parameters
Sejun Park, Jaeho Lee, Chulhee Yun, and Jinwoo Shin · 2020
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Memory capacity of neural networks with threshold and rectified linear unit activations
Roman Vershynin · 2020
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