activity
20182024
most citedTraining Neural Networks for Likelihood/Density Ratio Estimation

14 citations · 19 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Visual Semantic Parsing: From Images to Abstract Meaning Representation

Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu +4

The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e.g., image) into a structured representation, where…

cs.CV20201 cited

Image De-Quantization Using Generative Models as Priors

Kalliopi Basioti, George V. Moustakides

Image quantization is used in several applications aiming in reducing the number of available colors in an image and therefore its size. De-quantization is the task of reversing th…

eess.IV20203 cited

Image Restoration from Parametric Transformations using Generative Models

Kalliopi Basioti, George V. Moustakides

When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-…

eess.SP201914 cited

Training Neural Networks for Likelihood/Density Ratio Estimation

George V. Moustakides, Kalliopi Basioti

Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities. In classical approaches the two densities are…

cs.LG20191 cited

Optimizing Shallow Networks for Binary Classification

Kalliopi Basioti, George V. Moustakides

Data driven classification that relies on neural networks is based on optimization criteria that involve some form of distance between the output of the network and the desired lab…

eess.SP2019

Adaptive Blind Separation of Two Dependent Sources

George V. Moustakides, Feeby Salib, Kalliopi Basioti

We consider the problem of adaptive blind separation of two sources from their instantaneous mixtures. We focus on the case where the two sources are not necessarily independent. B…