Corpus ID: 225066839

PEP: Parameter Ensembling by Perturbation

@article{Mehrtash2020PEPPE,
  title={PEP: Parameter Ensembling by Perturbation},
  author={Alireza Mehrtash and P. Abolmaesumi and P. Golland and T. Kapur and D. Wassermann and W. M. Wells},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.12721}
}

Alireza Mehrtash, P. Abolmaesumi, +3 authors W. M. Wells

Published 2020

Physics, Computer Science, Mathematics

ArXiv

Ensembling is now recognized as an effective approach for increasing the predictive performance and calibration of deep networks. We introduce a new approach, Parameter Ensembling by Perturbation (PEP), that constructs an ensemble of parameter values as random perturbations of the optimal parameter set from training by a Gaussian with a single variance parameter. The variance is chosen to maximize the log-likelihood of the ensemble average ($\mathbb{L}$) on the validation data set. Empirically… CONTINUE READING

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