Efficient Bayesian Inference for Learning in the Ising Linear Perceptron and Signal Detection in CDMA
Disordered Systems and Neural Networks
2009-11-11 v2
Abstract
Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in Code Division Multiple Access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first order transition line that ends in a continuous phase transition point. Finite size effects are also studied.
Cite
@article{arxiv.cond-mat/0509763,
title = {Efficient Bayesian Inference for Learning in the Ising Linear Perceptron and Signal Detection in CDMA},
author = {Juan P. Neirotti and David Saad},
journal= {arXiv preprint arXiv:cond-mat/0509763},
year = {2009}
}
Comments
11 pages, 3 figures