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In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general.
Sarthak Jain and Byron C. Wallace. 2019 · 1902
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Sarthak Jain, Ramin Mohammadi, and Byron C Wallace. 2019 · 1904
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Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky. 1992 · 1992
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Practical recommendations for gradient-based training of deep architectures
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Adding gradient noise improves learning for very deep networks
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi S Jaakkola. 2017 · 2017
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Stochastic gradient descent as approximate bayesian inference
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Averaging weights leads to wider optima and better generalization
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