32 citations · 36 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Geometric Matrix Completion with Deep Conditional Random Fields
Duc Minh Nguyen, Robert Calderbank, Nikos Deligiannis
The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially, recommender systems. Existing matrix comple…
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.…