Search Results for author: Michael M. Bronstein

Found 54 papers, 26 papers with code

How does over-squashing affect the power of GNNs?

no code implementations6 Jun 2023 Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković

In this paper, we provide a rigorous analysis to determine which function classes of node features can be learned by an MPNN of a given capacity.

A Survey on Oversmoothing in Graph Neural Networks

no code implementations20 Mar 2023 T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra

Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth.

Graph Learning

Graph Neural Networks for Link Prediction with Subgraph Sketching

1 code implementation30 Sep 2022 Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire

Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.

Link Prediction

Understanding convolution on graphs via energies

2 code implementations22 Jun 2022 Francesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain, Thomas Markovich, Michael M. Bronstein

We do so by showing that linear graph convolutions with symmetric weights minimize a multi-particle energy that generalizes the Dirichlet energy; in this setting, the weight matrices induce edge-wise attraction (repulsion) through their positive (negative) eigenvalues, thereby controlling whether the features are being smoothed or sharpened.

Inductive Bias Node Classification

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

2 code implementations22 Jun 2022 Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs.

Learning to Infer Structures of Network Games

no code implementations16 Jun 2022 Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong

We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function.

Graph Anisotropic Diffusion

1 code implementation30 Apr 2022 Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Michael M. Bronstein

Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features.

Molecular Property Prediction Property Prediction

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

no code implementations1 Apr 2022 Kamilia Mullakaeva, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein

In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation.

Property Prediction

Graph-Coupled Oscillator Networks

1 code implementation4 Feb 2022 T. Konstantin Rusch, Benjamin P. Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein

This demonstrates that the proposed framework mitigates the oversmoothing problem.

Equivariant Subgraph Aggregation Networks

1 code implementation ICLR 2022 Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron

Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture.

Learning to Infer the Structure of Network Games

no code implementations29 Sep 2021 Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong

Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbors.

GRAND: Graph Neural Diffusion

1 code implementation NeurIPS Workshop DLDE 2021 Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Graph Learning

Fast End-to-End Learning on Protein Surfaces

1 code implementation CVPR 2021 Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein

These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

5 code implementations27 Apr 2021 Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods.

Protein Folding

Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales

no code implementations17 Apr 2021 Jacob Andreas, Gašper Beguš, Michael M. Bronstein, Roee Diamant, Denley Delaney, Shane Gero, Shafi Goldwasser, David F. Gruber, Sarah de Haas, Peter Malkin, Roger Payne, Giovanni Petri, Daniela Rus, Pratyusha Sharma, Dan Tchernov, Pernille Tønnesen, Antonio Torralba, Daniel Vogt, Robert J. Wood

We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data.

BIG-bench Machine Learning Translation

Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation

no code implementations16 Dec 2020 Mehdi Bahri, Eimear O' Sullivan, Shunwang Gong, Feng Liu, Xiaoming Liu, Michael M. Bronstein, Stefanos Zafeiriou

Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template.


Utilising Graph Machine Learning within Drug Discovery and Development

no code implementations9 Dec 2020 Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types.

BIG-bench Machine Learning Drug Discovery

Tuning Word2vec for Large Scale Recommendation Systems

no code implementations24 Sep 2020 Benjamin P. Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein

We show that applying constrained hy-perparameter optimization using only a 10% sample of the data still yields a 91%average improvement in hit rate over the default parameters when applied to thefull datasets.

Hyperparameter Optimization Recommendation Systems

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

2 code implementations16 Jun 2020 Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein

It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.

Graph Classification Graph Regression

Geometrically Principled Connections in Graph Neural Networks

no code implementations CVPR 2020 Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou

Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach.

Graph Classification

Transferability of Spectral Graph Convolutional Neural Networks

no code implementations30 Jul 2019 Ron Levie, Wei Huang, Lorenzo Bucci, Michael M. Bronstein, Gitta Kutyniok

Transferability, which is a certain type of generalization capability, can be loosely defined as follows: if two graphs describe the same phenomenon, then a single filter or ConvNet should have similar repercussions on both graphs.

Fake News Detection on Social Media using Geometric Deep Learning

3 code implementations10 Feb 2019 Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

Common Sense Reasoning Fact Checking +2

Isospectralization, or how to hear shape, style, and correspondence

1 code implementation CVPR 2019 Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà

The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.

Style Transfer

Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit

no code implementations19 Sep 2018 Stefan C. Schonsheck, Michael M. Bronstein, Rongjie Lai

In this paper, we propose a variational model to align the Laplace-Beltrami (LB) eigensytems of two non-isometric genus zero shapes via conformal deformations.

Graph Neural Networks for IceCube Signal Classification

1 code implementation17 Sep 2018 Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning.

Classification General Classification

Dual-Primal Graph Convolutional Networks

no code implementations3 Jun 2018 Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.

Graph Attention Recommendation Systems

MotifNet: a motif-based Graph Convolutional Network for directed graphs

no code implementations4 Feb 2018 Federico Monti, Karl Otness, Michael M. Bronstein

Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community.

BIG-bench Machine Learning

Dynamic Graph CNN for Learning on Point Clouds

17 code implementations24 Jan 2018 Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.

3D Part Segmentation 3D Semantic Segmentation +3


no code implementations ICLR 2018 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

Community Detection General Classification +2

Subspace Least Squares Multidimensional Scaling

no code implementations11 Sep 2017 Amit Boyarski, Alex M. Bronstein, Michael M. Bronstein

Multidimensional Scaling (MDS) is one of the most popular methods for dimensionality reduction and visualization of high dimensional data.

Computational Geometry

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

2 code implementations22 May 2017 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

Community Detection General Classification +3

Generative Convolutional Networks for Latent Fingerprint Reconstruction

no code implementations4 May 2017 Jan Svoboda, Federico Monti, Michael M. Bronstein

Performance of fingerprint recognition depends heavily on the extraction of minutiae points.

Geometric deep learning on graphs and manifolds using mixture model CNNs

4 code implementations CVPR 2017 Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.

Document Classification Graph Classification +8

Geometric deep learning: going beyond Euclidean data

no code implementations24 Nov 2016 Michael M. Bronstein, Joan Bruna, Yann Lecun, Arthur Szlam, Pierre Vandergheynst

In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques.

Learning shape correspondence with anisotropic convolutional neural networks

no code implementations NeurIPS 2016 Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein

Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications.

Efficient Globally Optimal 2D-to-3D Deformable Shape Matching

no code implementations CVPR 2016 Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers

We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface.

3D Shape Retrieval Retrieval

Partial Functional Correspondence

1 code implementation17 Jun 2015 Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein, Andrea Torsello, Daniel Cremers

In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes.

Geodesic convolutional neural networks on Riemannian manifolds

no code implementations26 Jan 2015 Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation.


Functional correspondence by matrix completion

no code implementations CVPR 2015 Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson, Pierre Vandergheynst

In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework.

Matrix Completion

Shape-from-intrinsic operator

no code implementations7 Jun 2014 Davide Boscaini, Davide Eynard, Michael M. Bronstein

Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations.

Sparse similarity-preserving hashing

no code implementations19 Dec 2013 Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro

In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.

Heat kernel coupling for multiple graph analysis

no code implementations11 Dec 2013 Michael M. Bronstein, Klaus Glashoff

In this paper, we introduce heat kernel coupling (HKC) as a method of constructing multimodal spectral geometry on weighted graphs of different size without vertex-wise bijective correspondence.

Structure-preserving color transformations using Laplacian commutativity

no code implementations1 Nov 2013 Davide Eynard, Artiom Kovnatsky, Michael M. Bronstein

Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people.

Making Laplacians commute

no code implementations19 Jul 2013 Michael M. Bronstein, Klaus Glashoff, Terry A. Loring

In this paper, we construct multimodal spectral geometry by finding a pair of closest commuting operators (CCO) to a given pair of Laplacians.

Clustering Dimensionality Reduction

WaldHash: sequential similarity-preserving hashing

no code implementations CIS 2010 Alexander M. Bronstein, Michael M. Bronstein, Leonidas J. Guibas, and Maks Ovsjanikov

Similarity-sensitive hashing seeks compact representation of vector data as binary codes, so that the Hamming distance between code words approximates the original similarity.

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