Paper Info
Title
Artificial Neural Network: Deep or Broad? An Empirical Study.
Abstract
Advent of Deep Learning and the emergence of Big Data has led to renewed interests in the study of Artificial Neural Networks (ANN). An ANN is a highly effective classifier that is capable of learning both linear and non-linear boundaries. The number of hidden layers and the number of nodes in each hidden layer (along with many other parameters) in an ANN, is considered to be a model selection problem. With success of deep learning especially on big datasets, there is a prevalent belief in machine learning community that a deep model (that is a model with many number of hidden layers) is preferable. However, this belies earlier theorems proved for ANN that only a single hidden layer (with multiple nodes) is capable of learning any arbitrary function, i.e., a shallow broad ANN. This raises the question of whether one should build a deep network or go for a broad network. In this paper, we do a systematic study of depth and breadth of an ANN in terms of its accuracy (0–1 Loss), bias, variance and convergence performance on 72 standard UCI datasets and we argue that broad ANN has better overall performance than deep ANN.
Year
Venue
Field
2016
Australasian Conference on Artificial Intelligence
Convergence (routing),Computer science,Convolutional neural network,Model selection,Artificial intelligence,Deep learning,Artificial neural network,Classifier (linguistics),Big data,Empirical research
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Liu Nian12810.48
Nayyar Abbas Zaidi2919.88