Search Results for author: Michaël Defferrard

Found 12 papers, 11 papers with code

NeuroSteiner: A Graph Transformer for Wirelength Estimation

no code implementations4 Jul 2024 Sahil Manchanda, Dana Kianfar, Markus Peschl, Romain Lepert, Michaël Defferrard

A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas.

Navigate

ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups

2 code implementations NeurIPS 2021 Hugo Aguettaz, Erik J. Bekkers, Michaël Defferrard

Surfing on the success of graph- and group-based neural networks, we take advantage of the recent developments in the geometric deep learning field to derive a new approach to exploit any anisotropies in data.

Graph Neural Network

Learning to recover orientations from projections in single-particle cryo-EM

2 code implementations NeurIPS 2021 Jelena Banjac, Laurène Donati, Michaël Defferrard

Our approach consists of two steps: (i) the estimation of distances between pairs of projections, and (ii) the recovery of the orientation of each projection from these distances.

Single Particle Analysis

DeepSphere: a graph-based spherical CNN

8 code implementations ICLR 2020 Michaël Defferrard, Martino Milani, Frédérick Gusset, Nathanaël Perraudin

DeepSphere, a method based on a graph representation of the sampled sphere, strikes a controllable balance between these two desiderata.

Simplicial Neural Networks

3 code implementations NeurIPS Workshop TDA_and_Beyond 2020 Stefania Ebli, Michaël Defferrard, Gard Spreemann

We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes.

Learning to Recognize Musical Genre from Audio

5 code implementations13 Mar 2018 Michaël Defferrard, Sharada P. Mohanty, Sean F. Carroll, Marcel Salathé

We here summarize our experience running a challenge with open data for musical genre recognition.

Music Genre Recognition

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

5 code implementations22 Dec 2016 Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data.

Language Modelling

FMA: A Dataset For Music Analysis

17 code implementations ISMIR 2017 Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson

We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections.

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

4 code implementations NeurIPS 2016 Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.

Node Classification Skeleton Based Action Recognition

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