• Publications
  • Influence
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
TLDR
This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
TLDR
This tutorial overview outlines the main challenges of the emerging field of signal processing on graphs, discusses different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlights the importance of incorporating the irregular structures of graph data domains when processing signals on graphs.
FREAK: Fast Retina Keypoint
TLDR
This work proposes a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK), which is in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK.
Geometric Deep Learning: Going beyond Euclidean data
TLDR
Deep neural networks are used for solving a broad range of problems from computer vision, natural-language processing, and audio analysis where the invariances of these structures are built into networks used to model them.
Fast Global Minimization of the Active Contour/Snake Model
TLDR
This paper proposes to unify three well-known image variational models, namely the snake model, the Rudin–Osher–Fatemi denoising model and the Mumford–Shah segmentation model, and establishes theorems with proofs to determine a global minimum of the active contour model.
Graph Signal Processing: Overview, Challenges, and Applications
TLDR
An overview of core ideas in GSP and their connection to conventional digital signal processing are provided, along with a brief historical perspective to highlight how concepts recently developed build on top of prior research in other areas.
Geodesic Convolutional Neural Networks on Riemannian Manifolds
TLDR
Geodesic Convolutional Neural Networks (GCNN), a generalization of the convolutional neural networks (CNN) paradigm to non-Euclidean manifolds is introduced, allowing to achieve state-of-the-art performance in problems such as shape description, retrieval, and correspondence.
Learning Laplacian Matrix in Smooth Graph Signal Representations
TLDR
This paper addresses the problem of learning graph Laplacians, which is equivalent to learning graph topologies, such that the input data form graph signals with smooth variations on the resulting topology and proposes an algorithm for learning graphs that enforces such property and is based on minimizing the variations of the signals on the learned graph.
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
TLDR
The proposed model combines convolutional neural networks on graphs to identify spatial structures and RNN to find dynamic patterns in data structured by an arbitrary graph.
...
1
2
3
4
5
...