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Investigating the Relationship Between Dropout Regularization and Model Complexity in Neural Networks
[article]
2021
arXiv
pre-print
Dropout Regularization, serving to reduce variance, is nearly ubiquitous in Deep Learning models. We explore the relationship between the dropout rate and model complexity by training 2,000 neural networks configured with random combinations of the dropout rate and the number of hidden units in each dense layer, on each of the three data sets we selected. The generated figures, with binary cross entropy loss and binary accuracy on the z-axis, question the common assumption that adding depth to
arXiv:2108.06628v2
fatcat:hiy4hfbj25axbia67nqtabkaea