Generative machine learning for robust free-space communication

Incremental adaptive networks implemented by free space optical (FSO) communication

The aim of this paper is to fully analyze the effects of free space optical (FSO) communication links on the estimation performance of the adaptive incremental networks. The FSO links in this paper are described with two turbulence models namely the Log-normal and Gamma-Gamma distributions. In order to investigate the impact of these models we produced the link coefficients using these distribu...

An efficient quantum algorithm for generative machine learning

A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer [1–3]. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup [4–8]. Several quantum algorithms for discriminative machine learning [9] have been found based on efficient...

Free Space Optical Communication for Tactical Operations

Hybrids of Generative and Discriminative Methods for Machine Learning

In machine learning, probabilistic models are described as belonging to one of two categories: generative or discriminative. Generative models are built to understand how samples from a particular category were generated. The category chosen for a new data-point is the category whose model fits the point best. Discriminative models are concerned with defining the boundaries between the categori...

Learning a generative failure-free PRISM clause

PRISM is a probabilistic logic programming formalism which allows learning parameters from examples through its graphical EM algorithm. PRISM is aimed at modelling generative processes in the compact first-order logic representation. It facilitates model selection by providing three scoring functions Bayesian Information Criterion (BIC), Cheeseman-Stutz (CS) and Variational free energy. This pa...