Nonparametric Weight Initialization of Neural Networks via Integral Representation
Authors: Sho Sonoda, Noboru Murata
ICLR 2014

Abstract: A new initialization method for hidden parameters in a neural network is proposed. Derived from the integral representation of the neural network, a nonparametric probability distribution of hidden parameters is introduced. In this proposal, hidden parameters are initialized by samples drawn from this distribution, and output parameters are fitted by ordinary linear regression. Numerical experiments show that backpropagation with proposed initialization converges faster than uniformly random initialization. Also it is shown that the proposed method achieves enough accuracy by itself without backpropagation in some cases.


Peer review:
[Review]:
In responce to our reviewer's comments, we have supplemented a detailed explanation of sampling procedure, and replaced the MNIST experiment with further investigated version.

The sampling algorithm runs as quick as sampling from ordinary distributions such as normal distribution.
The updated paper is to be online in a few days.