Search Results for author:
Found 13 papers, 2 papers with code
Deep Learning Sparse Ternary Projections for Compressed Sensing of Images
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.
Multiview Deep Learning for Predicting Twitter Users' Location
In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features.
Twitter User Geolocation using Deep Multiview Learning
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far.
Extendable Neural Matrix Completion
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.
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
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 With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
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.
Matrix Factorization via Deep Learning
Matrix completion is one of the key problems in signal processing and machine learning.
Geometric Matrix Completion with Deep Conditional Random Fields
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.
Rumour Detection via News Propagation Dynamics and User Representation Learning
Many methods have been introduced to detect rumours using the content or the social context of news.
Fake News Detection using Deep Markov Random Fields
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.
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
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.
Temporal Collaborative Filtering with Graph Convolutional Neural Networks
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.
Neural Multigrid Memory For Computational Fluid Dynamics
Turbulent flow simulation plays a crucial role in various applications, including aircraft and ship design, industrial process optimization, and weather prediction.
