Bronstein, Michael

28 publications

NeurIPS 2024 Fisher Flow Matching for Generative Modeling over Discrete Data Oscar Davis, Samuel Kessler, Mircea Petrache, İsmail İlkan Ceylan, Michael Bronstein, Avishek Joey Bose

TMLR 2024 How Does Over-Squashing Affect the Power of GNNs? Francesco Di Giovanni, T. Konstantin Rusch, Michael Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković

NeurIPS 2024 Learning on Large Graphs Using Intersecting Communities Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein, Ron Levie

NeurIPS 2024 Metric Flow Matching for Smooth Interpolations on the Data Manifold Kacper Kapuśniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael Bronstein, Avishek Joey Bose, Francesco Di Giovanni

NeurIPS 2024 Sequence-Augmented SE(3)-Flow Matching for Conditional Protein Generation Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, Alexander Tong, Avishek Joey Bose

UAI 2024 To Smooth a Cloud or to Pin It Down: Expressiveness Guarantees and Insights on Score Matching in Denoising Diffusion Models Teodora Reu, Francisco Vargas, Anna Kerekes, Michael Bronstein

NeurIPSW 2023 Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models Xuejin Zhang, Tomas Geffner, Matt McPartlon, Mehmet Akdel, Dylan Abramson, Graham Holt, Alexander Goncearenco, Luca Naef, Michael Bronstein

NeurIPS 2023 Curvature Filtrations for Graph Generative Model Evaluation Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck

NeurIPSW 2023 How Does Over-Squashing Affect the Power of GNNs? Francesco Di Giovanni, T. Konstantin Rusch, Michael Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković

NeurIPS 2023 Temporal Graph Benchmark for Machine Learning on Temporal Graphs Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Reihaneh Rabbany

NeurIPSW 2023 Towards Flexible, Efficient, and Effective Tensor Product Networks Nanxiang Wang, Chen Lin, Michael Bronstein, Philip Torr

ICML 2022 Graph-Coupled Oscillator Networks T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael Bronstein

ICML 2022 Learning to Infer Structures of Network Games Emanuele Rossi, Federico Monti, Yan Leng, Michael Bronstein, Xiaowen Dong

NeurIPS 2022 Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Cristian Bodnar, Francesco Di Giovanni, Benjamin Chamberlain, Pietro Lió, Michael Bronstein

NeurIPS 2022 Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries Fabrizio Frasca, Beatrice Bevilacqua, Michael Bronstein, Haggai Maron

NeurIPS 2021 Beltrami Flow and Neural Diffusion on Graphs Benjamin Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael Bronstein

ICML 2021 GRAND: Graph Neural Diffusion Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, Emanuele Rossi

NeurIPS 2021 Partition and Code: Learning How to Compress Graphs Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael Bronstein

JMLR 2021 Transferability of Spectral Graph Convolutional Neural Networks Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok

NeurIPS 2021 Weisfeiler and Lehman Go Cellular: CW Networks Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montufar, Michael Bronstein

ICML 2021 Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lió, Michael Bronstein

NeurIPS 2020 Fast Geometric Learning with Symbolic Matrices Jean Feydy, Alexis Glaunès, Benjamin Charlier, Michael Bronstein

ECCV 2020 The Average Mixing Kernel Signature Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello

ICLR 2019 PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks Jan Svoboda, Jonathan Masci, Federico Monti, Michael Bronstein, Leonidas Guibas

ICCV 2017 Deep Functional Maps: Structured Prediction for Dense Shape Correspondence Or Litany, Tal Remez, Emanuele Rodola, Alex Bronstein, Michael Bronstein

NeurIPS 2017 Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks Federico Monti, Michael Bronstein, Xavier Bresson

NeurIPS 2016 Learning Shape Correspondence with Anisotropic Convolutional Neural Networks Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael Bronstein

ICCV 2015 Robust Principal Component Analysis on Graphs Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst