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.
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.
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 • 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 • 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 • 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 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 • 4 Jul 2019 • Evaggelia Tsiligianni, Nikos Deligiannis
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements.
no code implementations • 18 Oct 2019 • Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis
Deep learning methods have been successfully applied to various computer vision tasks.
no code implementations • 21 Jan 2020 • Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis
The deep unfolding architecture is used as a core component of a multimodal framework for guided image super-resolution.
no code implementations • 7 Sep 2020 • Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis
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.