


default search action
Michael 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
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j70]Ilia Igashov
, Hannes Stärk, Clément Vignac, Arne Schneuing
, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia
:
Equivariant 3D-conditional diffusion model for molecular linker design. Nat. Mac. Intell. 6(4): 417-427 (2024) - [j69]Arne Schneuing, Charles Harris, Yuanqi Du
, Kieran Didi
, Arian Rokkum Jamasb, Ilia Igashov, Weitao Du, Carla P. Gomes
, Tom L. Blundell
, Pietro Lio
, Max Welling, Michael M. Bronstein, Bruno E. Correia
:
Structure-based drug design with equivariant diffusion models. Nat. Comput. Sci. 4(12): 899-909 (2024) - [j68]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. 2024 (2024) - [c151]Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni:
Locality-Aware Graph Rewiring in GNNs. ICLR 2024 - [c150]Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael M. Bronstein, Simone Scardapane, Paolo Di Lorenzo:
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module. ICLR 2024 - [c149]Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong:
SE(3)-Stochastic Flow Matching for Protein Backbone Generation. ICLR 2024 - [c148]Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia:
RetroBridge: Modeling Retrosynthesis with Markov Bridges. ICLR 2024 - [c147]Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka:
Position: Future Directions in the Theory of Graph Machine Learning. ICML 2024 - [c146]Ben Finkelshtein, Xingyue Huang, Michael M. Bronstein, Ismail Ilkan Ceylan:
Cooperative Graph Neural Networks. ICML 2024 - [c145]Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger:
Homomorphism Counts for Graph Neural Networks: All About That Basis. ICML 2024 - [c144]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position: Topological Deep Learning is the New Frontier for Relational Learning. ICML 2024 - [c143]Haitao Mao
, Jianan Zhao
, Xiaoxin He
, Zhikai Chen
, Qian Huang
, Zhaocheng Zhu
, Jian Tang
, Michael M. Bronstein
, Xavier Bresson
, Bryan Hooi
, Haiyang Zhang
, Xianfeng Tang
, Luo Chen
, Jiliang Tang
:
The 1st International Workshop on Graph Foundation Models (GFM). WWW (Companion Volume) 2024: 1789-1792 - [i143]Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner:
Setting the Record Straight on Transformer Oversmoothing. CoRR abs/2401.04301 (2024) - [i142]Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka:
Future Directions in Foundations of Graph Machine Learning. CoRR abs/2402.02287 (2024) - [i141]Xingyue Huang, Miguel A. Romero Orth, Pablo Barceló, Michael M. Bronstein, Ismail Ilkan Ceylan:
Link Prediction with Relational Hypergraphs. CoRR abs/2402.04062 (2024) - [i140]Chen Lin, Liheng Ma, Yiyang Chen, Wanli Ouyang, Michael M. Bronstein, Philip H. S. Torr:
Revealing Decurve Flows for Generalized Graph Propagation. CoRR abs/2402.08480 (2024) - [i139]Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger:
Homomorphism Counts for Graph Neural Networks: All About That Basis. CoRR abs/2402.08595 (2024) - [i138]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck
, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR abs/2402.08871 (2024) - [i137]Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot
, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio:
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR abs/2405.01616 (2024) - [i136]Joshua Southern, Francesco Di Giovanni, Michael M. Bronstein, Johannes F. Lutzeyer:
Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity. CoRR abs/2405.13526 (2024) - [i135]Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson:
Advancing Graph Convolutional Networks via General Spectral Wavelets. CoRR abs/2405.13806 (2024) - [i134]Oscar Davis, Samuel Kessler, Mircea Petrache, Ismail Ilkan Ceylan, Michael M. Bronstein, Avishek Joey Bose:
Fisher Flow Matching for Generative Modeling over Discrete Data. CoRR abs/2405.14664 (2024) - [i133]Kacper Kapusniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael M. Bronstein, Avishek Joey Bose, Francesco Di Giovanni:
Metric Flow Matching for Smooth Interpolations on the Data Manifold. CoRR abs/2405.14780 (2024) - [i132]T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus:
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks. CoRR abs/2405.15059 (2024) - [i131]Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein:
Bundle Neural Networks for message diffusion on graphs. CoRR abs/2405.15540 (2024) - [i130]Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose:
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation. CoRR abs/2405.20313 (2024) - [i129]Jianan Zhao, Hesham Mostafa, Mikhail Galkin, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang:
GraphAny: A Foundation Model for Node Classification on Any Graph. CoRR abs/2405.20445 (2024) - [i128]Ben Finkelshtein, Ismail Ilkan Ceylan, Michael M. Bronstein, Ron Levie:
Learning on Large Graphs using Intersecting Communities. CoRR abs/2405.20724 (2024) - [i127]Charlie Tan, Inés García-Redondo, Qiquan Wang, Michael M. Bronstein, Anthea Monod:
On the Limitations of Fractal Dimension as a Measure of Generalization. CoRR abs/2406.02234 (2024) - [i126]Baskaran Sripathmanathan, Xiaowen Dong, Michael M. Bronstein:
On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation. CoRR abs/2406.16816 (2024) - [i125]Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron:
Topological Blind Spots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. CoRR abs/2408.05486 (2024) - [i124]Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael M. Bronstein:
Neural Spacetimes for DAG Representation Learning. CoRR abs/2408.13885 (2024) - [i123]Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Cheng-Hao Liu, Sarthak Mittal, Nouha Dziri, Michael M. Bronstein, Yoshua Bengio, Pranam Chatterjee, Alexander Tong, Avishek Joey Bose:
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. CoRR abs/2410.08134 (2024) - [i122]Ahmed A. A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael M. Bronstein:
Relaxed Equivariance via Multitask Learning. CoRR abs/2410.17878 (2024) - [i121]Linus Bao, Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger:
Homomorphism Counts as Structural Encodings for Graph Learning. CoRR abs/2410.18676 (2024) - [i120]Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios, Michael M. Bronstein, Xiaowen Dong:
Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning. CoRR abs/2411.00835 (2024) - [i119]Benedict Aaron Tjandra, Federico Barbero, Michael M. Bronstein:
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification. CoRR abs/2411.03596 (2024) - 2023
- [j67]Kamilia Zaripova
, Luca Cosmo
, Anees Kazi, Seyed-Ahmad Ahmadi
, Michael M. Bronstein, Nassir Navab
:
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. Medical Image Anal. 88: 102839 (2023) - [j66]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) - [j65]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) - [j64]Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein:
Understanding convolution on graphs via energies. Trans. Mach. Learn. Res. 2023 (2023) - [c142]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. AAAI 2023: 9251-9259 - [c141]Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt:
Hyperbolic Deep Reinforcement Learning. ICLR 2023 - [c140]Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire:
Graph Neural Networks for Link Prediction with Subgraph Sketching. ICLR 2023 - [c139]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR 2023 - [c138]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. ICML 2023: 7865-7885 - [c137]Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. ICML 2023: 12252-12267 - [c136]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. LoG 2023: 25 - [c135]Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany:
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. NeurIPS 2023 - [c134]Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck:
Curvature Filtrations for Graph Generative Model Evaluation. NeurIPS 2023 - [c133]Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni:
Can strong structural encoding reduce the importance of Message Passing? TAG-ML 2023: 278-288 - [i118]Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck
:
Curvature Filtrations for Graph Generative Model Evaluation. CoRR abs/2301.12906 (2023) - [i117]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) - [i116]T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra:
A Survey on Oversmoothing in Graph Neural Networks. CoRR abs/2303.10993 (2023) - [i115]Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. CoRR abs/2305.08018 (2023) - [i114]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) - [i113]Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael M. Bronstein, Simone Scardapane, Paolo Di Lorenzo:
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module. CoRR abs/2305.16174 (2023) - [i112]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? CoRR abs/2306.03589 (2023) - [i111]Baskaran Sripathmanathan, Xiaowen Dong, Michael M. Bronstein:
On the Impact of Sample Size in Reconstructing Graph Signals. CoRR abs/2307.00336 (2023) - [i110]Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany:
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. CoRR abs/2307.01026 (2023) - [i109]António Leitão, Maxime Lucas, Simone Poetto, Taylor A. Hersh, Shane Gero, David F. Gruber, Michael M. Bronstein, Giovanni Petri:
Social learning across symbolic cultural barriers in non-human cultures. CoRR abs/2307.05304 (2023) - [i108]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera
, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian
, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i107]Ilia Igashov, Arne Schneuing
, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia:
RetroBridge: Modeling Retrosynthesis with Markov Bridges. CoRR abs/2308.16212 (2023) - [i106]Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang:
GraphText: Graph Reasoning in Text Space. CoRR abs/2310.01089 (2023) - [i105]Ben Finkelshtein, Xingyue Huang, Michael M. Bronstein, Ismail Ilkan Ceylan:
Cooperative Graph Neural Networks. CoRR abs/2310.01267 (2023) - [i104]Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni:
Locality-Aware Graph-Rewiring in GNNs. CoRR abs/2310.01668 (2023) - [i103]Avishek Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong
:
SE(3)-Stochastic Flow Matching for Protein Backbone Generation. CoRR abs/2310.02391 (2023) - [i102]Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Fan Nie, Michael M. Bronstein, Junchi Yan:
Advective Diffusion Transformers for Topological Generalization in Graph Learning. CoRR abs/2310.06417 (2023) - [i101]Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni:
Can strong structural encoding reduce the importance of Message Passing? CoRR abs/2310.15197 (2023) - [i100]Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein:
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR abs/2312.07511 (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) - [c132]Emanuele Rodolà, Luca Cosmo, Maks Ovsjanikov, Arianna Rampini, Simone Melzi, Michael M. Bronstein, Riccardo Marin:
Inverse Computational Spectral Geometry. Eurographics (Tutorials) 2022 - [c131]Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron:
Equivariant Subgraph Aggregation Networks. ICLR 2022 - [c130]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. ICLR 2022 - [c129]Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong:
Learning to Infer Structures of Network Games. ICML 2022: 18809-18827 - [c128]T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. ICML 2022: 18888-18909 - [c127]Michael M. Bronstein:
Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks. IMPROVE 2022: 7 - [c126]Ahmed El-Kishky, Michael M. Bronstein, Ying Xiao, Aria Haghighi:
Graph-based Representation Learning for Web-scale Recommender Systems. KDD 2022: 4784-4785 - [c125]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 - [c124]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 - [c123]Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. NeurIPS 2022 - [c122]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. TAG-ML 2022: 28-36 - [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]