Search Results for author: Duc Minh Nguyen

Found 12 papers, 1 papers with code

Temporal Collaborative Filtering with Graph Convolutional Neural Networks

no code implementations13 Oct 2020 Esther Rodrigo Bonet, Duc Minh Nguyen, Nikos Deligiannis

Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics.

Collaborative Filtering Recommendation Systems

Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization

no code implementations28 Aug 2020 Tien Huu Do, Duc Minh Nguyen, Giannis Bekoulis, Adrian Munteanu, Nikos Deligiannis

Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif; features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes' representations.

Data Augmentation Graph Classification

Fake News Detection using Deep Markov Random Fields

no code implementations NAACL 2019 Duc Minh Nguyen, Tien Huu Do, Robert Calderbank, Nikos Deligiannis

While the correlations among news articles have been shown to be effective cues for online news analysis, existing deep-learning-based methods often ignore this information and only consider each news article individually.

Fake News Detection

Geometric Matrix Completion with Deep Conditional Random Fields

no code implementations29 Jan 2019 Duc Minh Nguyen, Robert Calderbank, Nikos Deligiannis

We consider matrix completion as a structured prediction problem in a conditional random field (CRF), which is characterized by a maximum a posterior (MAP) inference, and we propose a deep model that predicts the missing entries by solving the MAP inference problem.

Matrix Completion Recommendation Systems +1

Matrix Factorization via Deep Learning

no code implementations4 Dec 2018 Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis

Matrix completion is one of the key problems in signal processing and machine learning.

Matrix Completion

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

no code implementations5 Nov 2018 Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis

Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e. g., meteorological and traffic information.

Air Quality Inference Matrix Completion

Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning

no code implementations4 Jul 2018 Duc Minh Nguyen, Evaggelia Tsiligianni, Robert Calderbank, Nikos Deligiannis

Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task.

Matrix Completion Multi-Task Learning

Extendable Neural Matrix Completion

no code implementations13 May 2018 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 recommender sys- tems.

Fine-tuning Matrix Completion

Twitter User Geolocation using Deep Multiview Learning

no code implementations11 May 2018 Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far.

Multiview Learning

Multiview Deep Learning for Predicting Twitter Users' Location

no code implementations21 Dec 2017 Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features.

Multiview Learning

Deep Learning Sparse Ternary Projections for Compressed Sensing of Images

1 code implementation28 Aug 2017 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 implemented by an appropriate projection matrix.

Cannot find the paper you are looking for? You can Submit a new open access paper.