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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. on PAMI 35(8), 1798–1828 (2013)
Denoeux, T., Lengellé, R.: Initializing back propagation networks with prototypes. Neural Networks 6(3), 351–363 (1993)
De Freitas, J.F.G., Niranjan, M., Gee, A.H., Doucet, A.: Sequential Monte Carlo methods to train neural network models. Neural Computation 12(4), 955–993 (2000)
Kůrková, V.: Complexity estimates based on integral transforms induced by computational units. Neural Networks 33, 160–167 (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 11, 2278–2324 (1998)
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient Backprop. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) NN: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 9–48. Springer, Heidelberg (2012)
LeCun, Y., Cortes, C.: The MNIST database of handwritten digits, http://yann.lecun.com/exdb/mnist/
Murata, N.: An integral representation of functions using three-layered networks and their approximation bounds. Neural Networks 9(6), 947–956 (1996)
Sprecher, D.A.: A numerical implementation of Kolmogorov’s superpositions. Neural Networks 9(5), 765–772 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sonoda, S., Murata, N. (2014). Sampling Hidden Parameters from Oracle Distribution. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_68
Download citation
DOI: https://doi.org/10.1007/978-3-319-11179-7_68
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
eBook Packages: Computer ScienceComputer Science (R0)