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Deep vs Wide


Currently, in machine learning, deep learning publications dominate over wide networks, but when not looking at current trends and concentrating only on precision (accuracy), the differences might not be as big as some might think.

DatasetDeep NetworksWide Networks
TIMIT & CIFAR-10Acoustic modeling using deep belief networksDo Deep Nets Really Need to be Deep?
NORB & CIFAR-10Learning methods for generic object recognition with invarianceto pose and lightingAn analysis of single-layer networks in unsupervised feature learning
MNIST & ADSStochastic pooling for regularization of deep convolutional neural networksLinear Regression on a Set of Selected Templates from a Pool of Randomly Generated Templates [under review]