We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
In this work, we consider the ISS (improved spread spectrum) watermarking [1] framework, and propose a generalized version of it, termed “Generalized Improved Spread Spectrum”...
Generalized Belief Propagation (gbp) has proven to be a promising technique for performing inference on Markov random fields (mrfs). However, its heavy computational cost and large...
A fundamental problem in signal processing is to estimate signal from noisy observations. When some prior information about the statistical models of the signal and noise is avail...
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and i...