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]|