5 papers
Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
Yigit Ugur, George Arvanitakis, Abdellatif Zaidi
In this paper, we develop an unsupervised generative clustering framework that combines the Variational Information Bottleneck and the Gaussian Mixture Model. Specifically, in our…
Vector Gaussian CEO Problem Under Logarithmic Loss
Yigit Ugur, Inaki Estella Aguerri, Abdellatif Zaidi
In this paper, we study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, agents observe independently c…
Vector Gaussian CEO Problem Under Logarithmic Loss and Applications
Yigit Ugur, Inaki Estella Aguerri, Abdellatif Zaidi
We study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, agents observe independently corrupted Gaussi…
A Generalization of Blahut-Arimoto Algorithm to Compute Rate-Distortion Regions of Multiterminal Source Coding Under Logarithmic Loss
Yigit Ugur, Inaki Estella Aguerri, Abdellatif Zaidi
In this paper, we present iterative algorithms that numerically compute the rate-distortion regions of two problems: the two-encoder multiterminal source coding problem and the Chi…
Reducing MIMO Detection Complexity via Hierarchical Modulation
Yigit Ugur, Ali Ozgur Yilmaz
This work considers multiple-input multiple-output (MIMO) communication systems using hierarchical modulation. A disadvantage of the maximum-likelihood (ML) MIMO detector is that c…