Abstract
A new sampling learning method for neural networks is proposed. Derived from an integral representation of neural networks, an oracle probability distribution of hidden parameters is introduced. In general rigorous sampling from the oracle distribution holds numerical difficulty, a linear-time sampling algorithm is also developed. Numerical experiments showed that when hidden parameters were initialized by the oracle distribution, following backpropagation converged faster to better parameters than when parameters were initialized by a normal distribution. © 2014 Springer International Publishing Switzerland.
Author supplied keywords
Cite
CITATION STYLE
Sonoda, S., & Murata, N. (2014). Sampling hidden parameters from oracle distribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 539–546). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_68
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
