M
Michaël Defferrard
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 24
Citations - 8646
Michaël Defferrard is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 12, co-authored 24 publications receiving 6349 citations.
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
TL;DR: In this article, a spectral graph theory formulation of convolutional neural networks (CNNs) was proposed to learn local, stationary, and compositional features on graphs, and the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs while being universal to any graph structure.
Proceedings Article
Convolutional neural networks on graphs with fast localized spectral filtering
TL;DR: 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.
Book ChapterDOI
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
TL;DR: 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.
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Structured Sequence Modeling with Graph Convolutional Recurrent Networks
TL;DR: Graph Convolutional Recurrent Network (GCRN) as mentioned in this paper is a deep learning model able to predict structured sequences of data, which can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling.
Proceedings Article
FMA: A Dataset for Music Analysis.
TL;DR: The Free Music Archive (FMA) dataset as mentioned in this paperMA is an open and easily accessible dataset which can be used to evaluate several tasks in music information retrieval (MIR), a field concerned with browsing, searching, and organizing large music collections.