1 code implementation • 21 Jun 2023 • Duc Minh Nguyen, Minh Chau Vu, Tuan Anh Nguyen, Tri Huynh, Nguyen Tri Nguyen, Truong Son Hy
Turbulent flow simulation plays a crucial role in various applications, including aircraft and ship design, industrial process optimization, and weather prediction.
no code implementations • 13 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.
no code implementations • 28 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.
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.
no code implementations • 18 Apr 2019 • Tien Huu Do, Xiao Luo, Duc Minh Nguyen, Nikos Deligiannis
Many methods have been introduced to detect rumours using the content or the social context of news.
no code implementations • 29 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.
no code implementations • 4 Dec 2018 • Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Matrix completion is one of the key problems in signal processing and machine learning.
no code implementations • 5 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.
no code implementations • 4 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.
no code implementations • 13 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.
no code implementations • 11 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.
no code implementations • 21 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.
1 code implementation • 28 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.