Title
New Error Bounds For Deep Relu Networks Using Sparse Grids
Abstract
We prove a theorem concerning the approximation of multivariate functions by deep ReLU networks. We present new error estimates for which the curse of dimensionality is lessened by establishing a connection with sparse grids.
Year
DOI
Venue
2019
10.1137/18M1189336
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
Keywords
DocType
Volume
machine learning, neural networks, deep networks, curse of dimensionality, sparse grids, approximation theory
Journal
1
Issue
Citations 
PageRank 
1
1
0.36
References 
Authors
0
2
Name
Order
Citations
PageRank
Hadrien Montanelli141.44
Qiang Du21692188.27