62 citations · 350 across the 33 of their papers we have counts for
7 papers · 1 filter
Temporal Collaborative Filtering with Graph Convolutional Neural Networks
Esther Rodrigo Bonet, Duc Minh Nguyen, Nikos Deligiannis
Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around i…
A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation
Huynh Van Luong, Boris Joukovsky, Yonina C. Eldar +1
Deep unfolded neural networks are designed by unrolling the iterations of optimization algorithms. They can be shown to achieve faster convergence and higher accuracy than their op…
A Review on Fact Extraction and Verification
Giannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis
We study the fact checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its acc…
Interpretable Deep Multimodal Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Multimodal image super-resolution (SR) is the reconstruction of a high resolution image given a low-resolution observation with the aid of another image modality. While existing de…
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
Tien Huu Do, Duc Minh Nguyen, Giannis Bekoulis +2
Graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many metho…
Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted - Minimization: Architecture Design and Generalization Analysis
Huynh Van Luong, Boris Joukovsky, Nikos Deligiannis
Deep unfolding methods---for example, the learned iterative shrinkage thresholding algorithm (LISTA)---design deep neural networks as learned variations of optimization methods. Th…
Multimodal Deep Unfolding for Guided Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
The reconstruction of a high resolution image given a low resolution observation is an ill-posed inverse problem in imaging. Deep learning methods rely on training data to learn an…