Deep vs. Diverse Architectures for Classification Problems

21 Aug 2017 Colleen M. Farrelly

This study compares various superlearner and deep learning architectures (machine-learning-based and neural-network-based) for classification problems across several simulated and industrial datasets to assess performance and computational efficiency, as both methods have nice theoretical convergence properties. Superlearner formulations outperform other methods at small to moderate sample sizes (500-2500) on nonlinear and mixed linear/nonlinear predictor relationship datasets, while deep neural networks perform well on linear predictor relationship datasets of all sizes... (read more)

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Methods used in the Paper

Affine Coupling
Bijective Transformation
Normalizing Flows
Distribution Approximation