Search Results for author: Michael Bronstein

Found 29 papers, 13 papers with code

The Average Mixing Kernel Signature

1 code implementation ECCV 2020 Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello

We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks.

Sheaf Neural Networks with Connection Laplacians

no code implementations17 Jun 2022 Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, Pietro Liò

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces.

Node Classification

Heterogeneous manifolds for curvature-aware graph embedding

no code implementations2 Feb 2022 Francesco Di Giovanni, Giulia Luise, Michael Bronstein

Graph embeddings, wherein the nodes of the graph are represented by points in a continuous space, are used in a broad range of Graph ML applications.

Graph Embedding

Graph Kernel Neural Networks

no code implementations14 Dec 2021 Luca Cosmo, Giorgia Minello, Michael Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.

Graph Classification

On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

no code implementations23 Nov 2021 Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael Bronstein

While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph.

Node Classification

Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling

no code implementations28 Sep 2021 Balder Croquet, Daan Christiaens, Seth M. Weinberg, Michael Bronstein, Dirk Vandermeulen, Peter Claes

The one-step registration model is benchmarked against iterative techniques, trading in a slightly lower performance in terms of shape fit for a higher compactness.

Fast geometric learning with symbolic matrices

no code implementations NeurIPS 2020 Jean Feydy, Joan Glaunès, Benjamin Charlier, Michael Bronstein

Geometric methods rely on tensors that can be encoded using a symbolic formula and data arrays, such as kernel and distance matrices.

Non-Rigid Puzzles

no code implementations26 Nov 2020 Or Litany, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Daniel Cremers

We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation.

Graph signal processing for machine learning: A review and new perspectives

no code implementations31 Jul 2020 Xiaowen Dong, Dorina Thanou, Laura Toni, Michael Bronstein, Pascal Frossard

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.

BIG-bench Machine Learning

Temporal Graph Networks for Deep Learning on Dynamic Graphs

5 code implementations18 Jun 2020 Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems.

Recommendation Systems

SIGN: Scalable Inception Graph Neural Networks

3 code implementations23 Apr 2020 Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti

Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.

Graph Representation Learning Graph Sampling +1

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

1 code implementation CVPR 2020 Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos, Michael Bronstein, Stefanos Zafeiriou

We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss.

3D Hand Pose Estimation

Latent-Graph Learning for Disease Prediction

no code implementations27 Mar 2020 Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.

Disease Prediction General Classification +1

Differentiable Graph Module (DGM) for Graph Convolutional Networks

1 code implementation11 Feb 2020 Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).

Disease Prediction Point Cloud Segmentation +1

Graph Attentional Autoencoder for Anticancer Hyperfood Prediction

no code implementations16 Jan 2020 Guadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, Kirill Veselkov, Michael Bronstein

Recent research efforts have shown the possibility to discover anticancer drug-like molecules in food from their effect on protein-protein interaction networks, opening a potential pathway to disease-beating diet design.

General Classification Graph Classification

SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator

1 code implementation13 Nov 2019 Shunwang Gong, Lei Chen, Michael Bronstein, Stefanos Zafeiriou

Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes.

3D Shape Reconstruction

ncRNA Classification with Graph Convolutional Networks

1 code implementation16 May 2019 Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro Liò

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.

Classification General Classification

Single Image 3D Hand Reconstruction with Mesh Convolutions

1 code implementation4 May 2019 Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael Bronstein, Stefanos Zafeiriou

In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes.

3D Reconstruction

MeshGAN: Non-linear 3D Morphable Models of Faces

no code implementations25 Mar 2019 Shiyang Cheng, Michael Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou

Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.

Efficient Deformable Shape Correspondence via Kernel Matching

1 code implementation25 Jul 2017 Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.

Matrix Completion on Graphs

2 code implementations7 Aug 2014 Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst

Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.

Ranked #14 on Recommendation Systems on MovieLens 100K (using extra training data)

Collaborative Filtering Matrix Completion +1

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