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ORCIDMichael M. Bronstein
Person information
- affiliation: University of Oxford, UK
- affiliation: Twitter
- affiliation: Imperial College London, Department of Computing, UK
- affiliation: Technion - Israel Institute of Technology, Haifa, Israel
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2020 – today
- 2023
[j65]Giorgos Bouritsas
, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein:
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 657-668 (2023)- [j64]Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein:
Differentiable Graph Module (DGM) for Graph Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1606-1617 (2023) - [i104]Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck:
Curvature Filtrations for Graph Generative Model Evaluation. CoRR abs/2301.12906 (2023) - [i103]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. CoRR abs/2302.02941 (2023) - [i102]T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra:
A Survey on Oversmoothing in Graph Neural Networks. CoRR abs/2303.10993 (2023) - [i101]Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. CoRR abs/2305.08018 (2023) - [i100]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR abs/2305.10498 (2023) - 2022
- [j63]Soha Sadat Mahdi, Harold Matthews, Nele Nauwelaers, Michiel Vanneste, Shunwang Gong, Giorgos Bouritsas, Gareth S. Baynam, Peter Hammond, Richard A. Spritz, Ophir D. Klein, Benedikt Hallgrímsson, Hilde Peeters, Michael M. Bronstein, Peter Claes:
Multi-Scale Part-Based Syndrome Classification of 3D Facial Images. IEEE Access 10: 23450-23462 (2022) - [j62]Luca Cosmo, Giorgia Minello, Michael M. Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello:
3D Shape Analysis Through a Quantum Lens: the Average Mixing Kernel Signature. Int. J. Comput. Vis. 130(6): 1474-1493 (2022) - [j61]Stefanos Zafeiriou, Michael M. Bronstein, Taco Cohen, Oriol Vinyals, Le Song, Jure Leskovec, Pietro Liò, Joan Bruna, Marco Gori:
Guest Editorial: Non-Euclidean Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(2): 723-726 (2022) - [j60]Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Susan Walsh, Mark D. Shriver, Michael M. Bronstein, Peter Claes:
Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach. IEEE Trans. Biom. Behav. Identity Sci. 4(2): 163-172 (2022) - [c131]Emanuele Rodolà, Luca Cosmo, Maks Ovsjanikov, Arianna Rampini, Simone Melzi, Michael M. Bronstein, Riccardo Marin:
Inverse Computational Spectral Geometry. Eurographics (Tutorials) 2022 - [c130]Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron:
Equivariant Subgraph Aggregation Networks. ICLR 2022 - [c129]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. ICLR 2022 - [c128]Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong:
Learning to Infer Structures of Network Games. ICML 2022: 18809-18827 - [c127]T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. ICML 2022: 18888-18909 - [c126]Michael M. Bronstein:
Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks. IMPROVE 2022: 7 - [c125]Ahmed El-Kishky, Michael M. Bronstein, Ying Xiao, Aria Haghighi:
Graph-based Representation Learning for Web-scale Recommender Systems. KDD 2022: 4784-4785 - [c124]Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. LoG 2022: 11 - [c123]Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein:
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS 2022 - [c122]Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. NeurIPS 2022 - [e9]Luigi Manfredi, Seyed-Ahmad Ahmadi, Michael M. Bronstein, Anees Kazi, Davide Lomanto, Alwyn Mathew, Ludovic Magerand, Kamilia Mullakaeva, Bartlomiej W. Papiez, Russell H. Taylor, Emanuele Trucco:
Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis - First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. Lecture Notes in Computer Science 13754, Springer 2022, ISBN 978-3-031-21082-2 [contents] - [i99]Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein:
Heterogeneous manifolds for curvature-aware graph embedding. CoRR abs/2202.01185 (2022) - [i98]T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. CoRR abs/2202.02296 (2022) - [i97]Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M. S, Cristian Regep, Jeremy B. R. Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio:
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro. CoRR abs/2202.04202 (2022) - [i96]Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein:
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. CoRR abs/2202.04579 (2022) - [i95]Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli:
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. CoRR abs/2202.06545 (2022) - [i94]Kamilia Mullakaeva, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein:
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. CoRR abs/2204.00323 (2022) - [i93]Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Michael M. Bronstein:
Graph Anisotropic Diffusion. CoRR abs/2205.00354 (2022) - [i92]Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong:
Learning to Infer Structures of Network Games. CoRR abs/2206.08119 (2022) - [i91]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. CoRR abs/2206.08702 (2022) - [i90]Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein:
Graph Neural Networks as Gradient Flows. CoRR abs/2206.10991 (2022) - [i89]Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. CoRR abs/2206.11140 (2022) - [i88]Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire:
Graph Neural Networks for Link Prediction with Subgraph Sketching. CoRR abs/2209.15486 (2022) - [i87]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR abs/2210.00513 (2022) - [i86]Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt:
Hyperbolic Deep Reinforcement Learning. CoRR abs/2210.01542 (2022) - [i85]Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design. CoRR abs/2210.05274 (2022) - [i84]Arne Schneuing, Yuanqi Du, Charles Harris, Arian R. Jamasb, Ilia Igashov, Weitao Du, Tom L. Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Structure-based Drug Design with Equivariant Diffusion Models. CoRR abs/2210.13695 (2022) - 2021
- [j59]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:
Utilizing graph machine learning within drug discovery and development. Briefings Bioinform. 22(6) (2021) - [j58]Filippo Maggioli, Simone Melzi, Maksim Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà:
Orthogonalized Fourier Polynomials for Signal Approximation and Transfer. Comput. Graph. Forum 40(2): 435-447 (2021) - [j57]Mehdi Bahri, Eimear O' Sullivan, Shunwang Gong, Feng Liu, Xiaoming Liu, Michael M. Bronstein, Stefanos Zafeiriou:
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation. Int. J. Comput. Vis. 129(9): 2680-2713 (2021) - [j56]Ron Levie, Wei Huang, Lorenzo Bucci, Michael M. Bronstein, Gitta Kutyniok:
Transferability of Spectral Graph Convolutional Neural Networks. J. Mach. Learn. Res. 22: 272:1-272:59 (2021) - [j55]Stefan C. Schonsheck, Michael M. Bronstein, Rongjie Lai:
Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit. J. Sci. Comput. 86(3): 30 (2021) - [c121]Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein:
Fast End-to-End Learning on Protein Surfaces. CVPR 2021: 15272-15281 - [c120]Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montúfar, Pietro Lió, Michael M. Bronstein:
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. ICML 2021: 1026-1037 - [c119]Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi:
GRAND: Graph Neural Diffusion. ICML 2021: 1407-1418 - [c118]Balder Croquet, Daan Christiaens, Seth M. Weinberg, Michael M. Bronstein, Dirk Vandermeulen, Peter Claes:
Unsupervised Diffeomorphic Surface Registration and Non-linear Modelling. MICCAI (4) 2021: 118-128 - [c117]Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. NeurIPS 2021: 1594-1609 - [c116]Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montúfar, Michael M. Bronstein:
Weisfeiler and Lehman Go Cellular: CW Networks. NeurIPS 2021: 2625-2640 - [c115]Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein:
Partition and Code: learning how to compress graphs. NeurIPS 2021: 18603-18619 - [c114]Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Kristian Lum, Jonathan Hunt, Michael M. Bronstein, Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Wenzhe Shi:
The 2021 RecSys Challenge Dataset: Fairness is not optional. RecSys Challenge 2021: 1-6 - [c113]Thibaut Thonet, Stéphane Clinchant, Carlos Lassance, Elvin Isufi, Jiaqi Ma, Yutong Xie, Jean-Michel Renders, Michael M. Bronstein:
GReS: Workshop on Graph Neural Networks for Recommendation and Search. RecSys 2021: 780-782 - [c112]Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Jonathan Hunt, Michael M. Bronstein, Wenzhe Shi:
RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter's Home Timeline. RecSys 2021: 819-824 - [i83]Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael M. Bronstein:
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. CoRR abs/2103.03212 (2021) - [i82]Jacob Andreas, Gasper Begus, 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 M. Vogt, Robert J. Wood:
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales. CoRR abs/2104.08614 (2021) - [i81]Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic:
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR abs/2104.13478 (2021) - [i80]Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein:
GRAND: Graph Neural Diffusion. CoRR abs/2106.10934 (2021) - [i79]Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael M. Bronstein:
Weisfeiler and Lehman Go Cellular: CW Networks. CoRR abs/2106.12575 (2021) - [i78]Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein:
Partition and Code: learning how to compress graphs. CoRR abs/2107.01952 (2021) - [i77]Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Kristian Lum, Jonathan Hunt, Michael M. Bronstein, Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Wenzhe Shi:
The 2021 RecSys Challenge Dataset: Fairness is not optional. CoRR abs/2109.08245 (2021) - [i76]Balder Croquet, Daan Christiaens, Seth M. Weinberg, Michael M. Bronstein, Dirk Vandermeulen, Peter Claes:
Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling. CoRR abs/2109.13630 (2021) - [i75]Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron:
Equivariant Subgraph Aggregation Networks. CoRR abs/2110.02910 (2021) - [i74]Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. CoRR abs/2110.09443 (2021) - [i73]Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features. CoRR abs/2111.12128 (2021) - [i72]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. CoRR abs/2111.14522 (2021) - [i71]Luca Cosmo, Giorgia Minello, Michael M. Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello:
Graph Kernel Neural Networks. CoRR abs/2112.07436 (2021) - 2020
- [j54]Aviad Zabatani, Vitaly Surazhsky, Erez Sperling, Sagi Ben-Moshe, Ohad Menashe, David H. Silver, Zachi Karni, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel:
Intel® RealSense™ SR300 Coded Light Depth Camera. IEEE Trans. Pattern Anal. Mach. Intell. 42(10): 2333-2345 (2020) - [j53]Xiaowen Dong, Dorina Thanou, Laura Toni, Michael M. Bronstein, Pascal Frossard:
Graph Signal Processing for Machine Learning: A Review and New Perspectives. IEEE Signal Process. Mag. 37(6): 117-127 (2020) - [c111]Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos, Michael M. Bronstein, Stefanos Zafeiriou:
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild. CVPR 2020: 4989-4999 - [c110]Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou:
Geometrically Principled Connections in Graph Neural Networks. CVPR 2020: 11412-11421 - [c109]Luca Cosmo, Giorgia Minello, Michael M. Bronstein, Luca Rossi, Andrea Torsello:
The Average Mixing Kernel Signature. ECCV (20) 2020: 1-17 - [c108]Jan Svoboda, Pietro Astolfi, Davide Boscaini, Jonathan Masci, Michael M. Bronstein:
Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition. IJCB 2020: 1-9 - [c107]Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Sergiy Bokhnyak, Susan Walsh, Mark D. Shriver, Michael M. Bronstein, Peter Claes:
3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties. ICPR 2020: 1757-1764 - [c106]Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein:
Latent-Graph Learning for Disease Prediction. MICCAI (2) 2020: 643-653 - [c105]Jean Feydy, Joan Alexis Glaunès, Benjamin Charlier, Michael M. Bronstein:
Fast geometric learning with symbolic matrices. NeurIPS 2020 - [c104]Vito Walter Anelli, Amra Delic, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael M. Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, Wenzhe Shi:
RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home Timeline. RecSys 2020: 623-627 - [c103]Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein:
Tuning Word2vec for Large Scale Recommendation Systems. RecSys 2020: 732-737 - [i70]Guadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, Kirill A. Veselkov, Michael M. Bronstein:
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction. CoRR abs/2001.05724 (2020) - [i69]Anees Kazi, Luca Cosmo, Nassir Navab, Michael M. Bronstein:
Differentiable Graph Module (DGM) Graph Convolutional Networks. CoRR abs/2002.04999 (2020) - [i68]David Pickup, Xianfang Sun, Paul L. Rosin, Ralph R. Martin, Z. Cheng, Zhouhui Lian, Masaki Aono, A. Ben Hamza, Alexander M. Bronstein, Michael M. Bronstein, S. Bu, Umberto Castellani, S. Cheng, Valeria Garro, Andrea Giachetti, Afzal Godil, Luca Isaia, J. Han, Henry Johan, Long Lai, Bo Li, Chunyuan Li, Haisheng Li, Roee Litman, X. Liu, Z. Liu, Yijuan Lu, Li Sun, Gary K. L. Tam, Atsushi Tatsuma, Jianbo Ye:
Shape retrieval of non-rigid 3d human models. CoRR abs/2003.08763 (2020) - [i67]Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein:
Latent Patient Network Learning for Automatic Diagnosis. CoRR abs/2003.13620 (2020) - [i66]Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos, Michael M. Bronstein, Stefanos Zafeiriou:
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild. CoRR abs/2004.01946 (2020) - [i65]Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou:
Geometrically Principled Connections in Graph Neural Networks. CoRR abs/2004.02658 (2020) - [i64]Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, Federico Monti:
SIGN: Scalable Inception Graph Neural Networks. CoRR abs/2004.11198 (2020) - [i63]Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fong, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael M. Bronstein, Amra Delic, Gabriele Sottocornola, Vito Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi:
Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline. CoRR abs/2004.13715 (2020) - [i62]Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein:
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. CoRR abs/2006.09252 (2020) - [i61]Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael M. Bronstein:
Temporal Graph Networks for Deep Learning on Dynamic Graphs. CoRR abs/2006.10637 (2020) - [i60]Xiaowen Dong, Dorina Thanou, Laura Toni, Michael M. Bronstein, Pascal Frossard:
Graph signal processing for machine learning: A review and new perspectives. CoRR abs/2007.16061 (2020) - [i59]Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Sergiy Bokhnyak, Susan Walsh, Mark D. Shriver, Michael M. Bronstein, Peter Claes:
3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties. CoRR abs/2009.04746 (2020) - [i58]Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein:
Tuning Word2vec for Large Scale Recommendation Systems. CoRR abs/2009.12192 (2020) - [i57]Or Litany, Emanuele Rodolà, Alex M. Bronstein, Michael M. Bronstein, Daniel Cremers:
Non-Rigid Puzzles. CoRR abs/2011.13076 (2020) - [i56]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:
Utilising Graph Machine Learning within Drug Discovery and Development. CoRR abs/2012.05716 (2020) - [i55]Mehdi Bahri, Eimear O' Sullivan, Shunwang Gong, Feng Liu, Xiaoming Liu, Michael M. Bronstein, Stefanos Zafeiriou:
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation. CoRR abs/2012.09235 (2020)
2010 – 2019
- 2019
- [j52]Emanuele Rodolà, Zorah Lähner, Alexander M. Bronstein, Michael M. Bronstein, Justin Solomon:
Functional Maps Representation On Product Manifolds. Comput. Graph. Forum 38(1): 678-689 (2019) - [j51]Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon:
Dynamic Graph CNN for Learning on Point Clouds. ACM Trans. Graph. 38(5): 146:1-146:12 (2019) - [j50]Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein:
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters. IEEE Trans. Signal Process. 67(1): 97-109 (2019) - [c102]Roberto M. Dyke, C. Stride, Yu-Kun Lai, Paul L. Rosin, Mathieu Aubry, Amit Boyarski, Alexander M. Bronstein, Michael M. Bronstein, Daniel Cremers, Matthew Fisher, Thibault Groueix, Daoliang Guo, Vladimir G. Kim, Ron Kimmel, Zorah Lähner, Kun Li, Or Litany, Tal Remez, Emanuele Rodolà, Bryan C. Russell, Yusuf Sahillioglu, Ron Slossberg, Gary K. L. Tam, Matthias Vestner, Z. Wu, Jingyu Yang:
Shape Correspondence with Isometric and Non-Isometric Deformations. 3DOR@Eurographics 2019: 111-119 - [c101]Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael M. Bronstein, Stefanos Zafeiriou:
Single Image 3D Hand Reconstruction with Mesh Convolutions. BMVC 2019: 45 - [c100]Simone Melzi, Riccardo Spezialetti, Federico Tombari, Michael M. Bronstein, Luigi Di Stefano, Emanuele Rodolà:
GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching. CVPR 2019: 4629-4638 - [c99]Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà:
Isospectralization, or How to Hear Shape, Style, and Correspondence. CVPR 2019: 7529-7538 - [c98]Michael M. Bronstein, Leonidas J. Guibas, Iasonas Kokkinos, Or Litany, Niloy J. Mitra, Federico Monti, Emanuele Rodolà:
Deep Learning for Computer Graphics and Geometry Processing. Eurographics (Tutorials) 2019: 43 - [c97]Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Stefanos Zafeiriou, Michael M. Bronstein:
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation. ICCV 2019: 7212-7221 - [c96]Shunwang Gong, Lei Chen, Michael M. Bronstein, Stefanos Zafeiriou:
SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. ICCV Workshops 2019: 4141-4148 - [c95]Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas J. Guibas:
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. ICLR (Poster) 2019 - [i54]Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein:
Fake News Detection on Social Media using Geometric Deep Learning. CoRR abs/1902.06673 (2019) - [i53]Shiyang Cheng, Michael M. Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou:
MeshGAN: Non-linear 3D Morphable Models of Faces. CoRR abs/1903.10384 (2019) - [i52]Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael M. Bronstein, Stefanos Zafeiriou:
Single Image 3D Hand Reconstruction with Mesh Convolutions. CoRR abs/1905.01326 (2019) - [i51]Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael M. Bronstein, Stefanos Zafeiriou:
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation. CoRR abs/1905.02876 (2019) - [i50]Emanuele Rossi, Federico Monti, Michael M. Bronstein, Pietro Liò:
ncRNA Classification with Graph Convolutional Networks. CoRR abs/1905.06515 (2019) - [i49]Ron Levie, Michael M. Bronstein, Gitta Kutyniok:
Transferability of Spectral Graph Convolutional Neural Networks. CoRR abs/1907.12972 (2019) - [i48]