Search Results for author: Evaggelia Tsiligianni

Found 11 papers, 1 papers with code

Interpretable Deep Multimodal Image Super-Resolution

no code implementations7 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.

Image Super-Resolution

Multimodal Deep Unfolding for Guided Image Super-Resolution

no code implementations21 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.

Image Super-Resolution Multimodal Deep Learning

Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information

no code implementations4 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.

Multimodal Deep Learning Representation Learning

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.

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