International audienceA number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connec-tivity. To move beyond a simple diffusion, filter weights are conditioned on the specific edge labels in the neighborhood of a vertex. Together with the proper choice of graph coarsening, we explore constructing deep neural networks for graph classification. In particular, we demonstrate the generality of our formulation in point cloud classification , where we set the new state of the art, and on a graph classification dataset, where we outperform other de...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent ...
Point clouds provide a flexible geometric representation suitable for countless applications in comp...
International audienceA number of problems can be formulated as prediction on graph-structured data....
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and field...
Graph representation learning serves as the core of many important tasks on graphs, ranging from fri...
Recently, many researchers have been focusing on the definition of neural networks for graphs. The b...
Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department ...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
The benefit of localized features within the regular domain has given rise to the use of Convolution...
Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose ...
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle gra...
Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggreg...
In this ongoing work, we describe several architectures that generalize convolutional neural network...
Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent ...
Point clouds provide a flexible geometric representation suitable for countless applications in comp...
International audienceA number of problems can be formulated as prediction on graph-structured data....
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and field...
Graph representation learning serves as the core of many important tasks on graphs, ranging from fri...
Recently, many researchers have been focusing on the definition of neural networks for graphs. The b...
Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department ...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
The benefit of localized features within the regular domain has given rise to the use of Convolution...
Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose ...
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle gra...
Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggreg...
In this ongoing work, we describe several architectures that generalize convolutional neural network...
Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than...
The last half-decade has seen a surge in deep learning research on irregular domains and efforts to ...
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent ...
Point clouds provide a flexible geometric representation suitable for countless applications in comp...