@inproceedings{1287cd724f4e4a17ac16e2e4eca43308,
title = "Learning a continuous hidden variable model for binary data",
abstract = "A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinear-ity distinguishes the model from conventional principal components analysis. The relationships between the correlations of the underlying continuous Gaussian variables and the binary output variables are utilized to learn the appropriate weights of the network. The advantages of this approach are illustrated on a translationally invariant binary distribution and on handwritten digit images.",
author = "Lee, {Daniel D.} and Haim Sompolinsky",
year = "1999",
language = "אנגלית",
isbn = "0262112450",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "515--521",
booktitle = "Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998",
note = "12th Annual Conference on Neural Information Processing Systems, NIPS 1998 ; Conference date: 30-11-1998 Through 05-12-1998",
}