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.

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