Artificial intelligence, Algorithm, Theoretical computer science, Wavelet and Spectral graph theory are his primary areas of study. His Artificial intelligence research incorporates elements of Computational complexity theory, Computer vision and Pattern recognition. He has researched Algorithm in several fields, including Discrete mathematics, Imaging phantom, Excitation and Mathematical optimization.
The Theoretical computer science study combines topics in areas such as Graph theory, Deep learning, Topological graph theory and Graph. His research integrates issues of Mathematical analysis and Stereographic projection in his study of Wavelet. His work in Spectral graph theory covers topics such as 1-planar graph which are related to areas like Leverage, Spectral density, Wiener filter and Stationary process.
His primary areas of study are Artificial intelligence, Algorithm, Computer vision, Wavelet and Pattern recognition. Matching pursuit, Iterative reconstruction, Compressed sensing, Sparse approximation and Image processing are subfields of Artificial intelligence in which his conducts study. His research in Compressed sensing intersects with topics in Spread spectrum, Electronic engineering, Fourier transform and Convex optimization.
His Algorithm research integrates issues from Theoretical computer science, Mathematical optimization, Graph and Signal processing. His biological study spans a wide range of topics, including Inverse problem and Omnidirectional antenna. His Wavelet study combines topics in areas such as Mathematical analysis and Cosmic microwave background.
His main research concerns Algorithm, Artificial intelligence, Graph, Graph and Theoretical computer science. His Algorithm research integrates issues from Discrete mathematics, Matrix, Spectral graph theory, Graph based and Signal processing. His research in Artificial intelligence intersects with topics in Computer vision and Pattern recognition.
His studies deal with areas such as Matrix decomposition, Spectral clustering, Eigenvalues and eigenvectors and Data mining as well as Graph. His biological study spans a wide range of topics, including Embedding, Recommender system, Deep learning and Generalization. His Embedding research is multidisciplinary, relying on both Computational complexity theory and Connectome.
Pierre Vandergheynst mainly investigates Graph, Theoretical computer science, Artificial intelligence, Algorithm and Spectral graph theory. Within one scientific family, he focuses on topics pertaining to Euclidean geometry under Graph, and may sometimes address concerns connected to Upsampling, Data domain, Graph bandwidth and Graph reduction. He interconnects Matrix decomposition, Non-negative matrix factorization, Recommender system, Collaborative filtering and Matrix completion in the investigation of issues within Theoretical computer science.
His Artificial intelligence research includes elements of Computational complexity theory and Pattern recognition. His work deals with themes such as Discrete mathematics, Time–frequency analysis and Signal processing, which intersect with Algorithm. His Spectral graph theory research incorporates elements of 1-planar graph, Bandlimiting and Random graph.
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard;Xavier Bresson;Pierre Vandergheynst.
neural information processing systems (2016)
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman;Sunil K. Narang;Pascal Frossard;Antonio Ortega.
IEEE Signal Processing Magazine (2013)
FREAK: Fast Retina Keypoint
Alexandre Alahi;Raphael Ortiz;Pierre Vandergheynst.
computer vision and pattern recognition (2012)
Geometric Deep Learning: Going beyond Euclidean data
Michael M. Bronstein;Joan Bruna;Yann LeCun;Arthur Szlam.
IEEE Signal Processing Magazine (2017)
Wavelets on graphs via spectral graph theory
David K. Hammond;Pierre Vandergheynst;Rémi Gribonval.
Applied and Computational Harmonic Analysis (2011)
Fast Global Minimization of the Active Contour/Snake Model
Xavier Bresson;Selim Esedoglu;Pierre Vandergheynst;Jean-Philippe Thiran.
Journal of Mathematical Imaging and Vision (2007)
Graph Signal Processing: Overview, Challenges, and Applications
Antonio Ortega;Pascal Frossard;Jelena Kovacevic;Jose M. F. Moura.
Proceedings of the IEEE (2018)
Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes
H. Mamaghanian;N. Khaled;D. Atienza;P. Vandergheynst.
IEEE Transactions on Biomedical Engineering (2011)
Compressed Sensing and Redundant Dictionaries
H. Rauhut;K. Schnass;P. Vandergheynst.
IEEE Transactions on Information Theory (2008)
Geodesic Convolutional Neural Networks on Riemannian Manifolds
Jonathan Masci;Davide Boscaini;Michael M. Bronstein;Pierre Vandergheynst.
international conference on computer vision (2015)
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