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Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks
Training a deep neural network (DNN) often involves stochastic optimization, which means each run will produce a different model.
Robust Hierarchical-Optimization RLS Against Sparse Outliers
This paper fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models.
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