Work

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review

Year: 2017

Type: article

Abstract: The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.

Source: Machine Intelligence Research

DOI: 10.1007/s11633-017-1054-2

Authors: Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao

Institutions: McGovern Institute for Brain Research, Massachusetts Institute of Technology, California Institute of Technology, Claremont Graduate University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: National Science Foundation, Companhia Brasileira de Metalurgia e Mineração, Army Research Office