32 citations · 72 across the 11 of their papers we have counts for
11 papers
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…
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…
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge abo…
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information
Evaggelia Tsiligianni, Nikos Deligiannis
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements. Typically, the recovery relies on complex numerical o…
Matrix Factorization via Deep Learning
Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix…
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +5
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial…
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
Duc Minh Nguyen, Evaggelia Tsiligianni, Robert Calderbank +1
Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Neverth…
Extendable Neural Matrix Completion
Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and r…
Twitter User Geolocation using Deep Multiview Learning
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +2
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows…
Multiview Deep Learning for Predicting Twitter Users' Location
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +2
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing.…
Deep Learning Sparse Ternary Projections for Compressed Sensing of Images
Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism…