32 citations · 72 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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.…