Paper Info
Title
On the Power of Over-parametrization in Neural Networks with Quadratic Activation.
Abstract
We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k ge sqrt{2n}$, over-parametrization enables local search algorithms to find a emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $kge sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks.
Year
Venue
DocType
2018
ICML
Conference
Volume
Citations 
PageRank 
abs/1803.01206
20
0.63
References 
Authors
38
2
Name
Order
Citations
PageRank
Simon Du121029.79
Lee, Jason D.271148.29