
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predomin...
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NonRigid Puzzles
Shape correspondence is a fundamental problem in computer graphics and v...
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3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric FusionNet of Demographic Properties
Face recognition is a widely accepted biometric verification tool, as th...
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Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of...
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
Graph Neural Networks (GNNs) have recently become increasingly popular d...
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PrivacyPreserving Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network pl...
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SIGN: Scalable Inception Graph Neural Networks
Geometric deep learning, a novel class of machine learning algorithms ex...
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WeaklySupervised MeshConvolutional Hand Reconstruction in the Wild
We introduce a simple and effective network architecture for monocular 3...
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Latent Patient Network Learning for Automatic Diagnosis
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerfu...
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Differentiable Graph Module (DGM) Graph Convolutional Networks
Graph deep learning has recently emerged as a powerful ML concept allowi...
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Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
Recent research efforts have shown the possibility to discover anticance...
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SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Intrinsic graph convolution operators with differentiable kernel functio...
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ncRNA Classification with Graph Convolutional Networks
Noncoding RNA (ncRNA) are RNA sequences which don't code for a gene but...
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Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Generative models for 3D geometric data arise in many important applicat...
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Single Image 3D Hand Reconstruction with Mesh Convolutions
Monocular 3D reconstruction of deformable objects, such as human body pa...
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MeshGAN: Nonlinear 3D Morphable Models of Faces
Generative Adversarial Networks (GANs) are currently the method of choic...
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Deformable Shape Completion with Graph Convolutional Autoencoders
The availability of affordable and portable depth sensors has made scann...
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Efficient Deformable Shape Correspondence via Kernel Matching
We present a method to match three dimensional shapes under nonisometri...
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Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for line...
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Matrix Completion on Graphs
The problem of finding the missing values of a matrix given a few of its...
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Michael Bronstein
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Advisor at Endor Software Ltd, Cofounder, Chief Scientist at Fabula AI, Visiting Scholar at Harvard John A. Paulson School of Engineering and Applied Sciences, Radcliffe fellow at Radcliffe Institute for Advanced Study at Harvard University, Research Affiliate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Rudolf Diesel industrial fellow at Technical University Munich, Visiting Professor at Tel Aviv University, CoFounder at Videocites, Principal engineer at Intel Corporation, Professor at USI Università della Svizzera italiana, Partner, cofounder at BBK Technologies, Visiting Professor at University of Verona 2014