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The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models.
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What is the effect of importance weighting in deep learning?
Jonathon Byrd and Zachary Lipton · 2019
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Class-balanced loss based on effective number of samples
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Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2018
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Gradient descent maximizes the margin of homogeneous neural networks
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Lexicographic and depth-sensitive margins in homogeneous and non-homogeneous deep models
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Regularization matters: Generalization and optimization of neural nets vs their induced kernel
Colin Wei, Jason D Lee, Qiang Liu, and Tengyu Ma · 2019
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Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss
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Rethinking importance weighting for deep learning under distribution shift
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