@inproceedings{9dcf034f1cba48e886c9d62855f72809,
title = "Sampling hidden parameters from oracle distribution",
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.",
keywords = "Integral representation, backpropagation, neural networks, oracle distribution, sampling learning, weight initialization",
author = "Sho Sonoda and Noboru Murata",
year = "2014",
doi = "10.1007/978-3-319-11179-7_68",
language = "English",
isbn = "9783319111780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "539--546",
booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings",
note = "24th International Conference on Artificial Neural Networks, ICANN 2014 ; Conference date: 15-09-2014 Through 19-09-2014",
}