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Michael Bronstein

27 papers · 2015–2024 · 9 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+11 more ↓ πŸ—ΊοΈ Taxonomy Completionist (45) 🌍 Conference Polyglot Β· Silver πŸƒ Academic Marathon Β· Sprint πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
πŸ“› The Namer πŸ”¬ Deep Specialist (16) πŸ† Grand Slam πŸ† Keyword Champion (2) 🧭 Keyword Pioneer πŸ“ˆ Trend Setter ⚑ Prolific Year Β· Productive πŸ—ƒοΈ Keyword Collector (139) πŸ”₯ Unstoppable Β· Silver πŸ’Ž Century Club Β· Bronze 🌈 Renaissance Researcher (6)

Venues

NeurIPS (14) ICML (4) ICCV (3) AAAI (1) CVPR (1) ECCV (1) ICLR (1) JMLR (1) UAI (1)

Top co-authors

Emanuele Rossi (4) Cristian Bodnar (3) Pietro LiΓ³ (3) Federico Monti (3) Avishek Joey Bose (3) James Rowbottom (3) Francesco Di Giovanni (3) Fabrizio Frasca (3) Nina Otter (2) Alex Bronstein (2)

Research topics

Deep Learning (21) Machine Learning (18) Architectures (17) Core Methods (13) Optimization & Theory (7) Mathematics & Optimization (6) Models (6) Artificial Intelligence (5) Mathematics (5) Core AI (4) Application Areas (3) Learning Types (3) Techniques (3) Computer Science (2) Computer Vision (2)

Keywords

graph neural network (11) message passing (4) weisfeiler-lehman test (3) generative model (3) graph learning (3) flow matching (3) neural diffusion (2) node classification (2) simplicial complex (2) graph convolutional network (2) topological data analysis (2) geometric deep learning (2) graph regularization (2) sample complexity (1) model evaluation (1) feature learning (1) graph representation learning (1) optimal transport (1) link prediction (1) transformer architecture (1)

Papers

Fisher Flow Matching for Generative Modeling over Discrete Data NeurIPS 2024 Learning on Large Graphs using Intersecting Communities NeurIPS 2024 Metric Flow Matching for Smooth Interpolations on the Data Manifold NeurIPS 2024 Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation NeurIPS 2024 To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models UAI 2024 Curvature Filtrations for Graph Generative Model Evaluation NeurIPS 2023 Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization AAAI 2023 Temporal Graph Benchmark for Machine Learning on Temporal Graphs NeurIPS 2023 Graph-Coupled Oscillator Networks ICML 2022 Learning to Infer Structures of Network Games ICML 2022 Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs NeurIPS 2022 Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries NeurIPS 2022 Beltrami Flow and Neural Diffusion on Graphs NeurIPS 2021 GRAND: Graph Neural Diffusion ICML 2021 Partition and Code: learning how to compress graphs NeurIPS 2021 Transferability of Spectral Graph Convolutional Neural Networks JMLR 2021 Weisfeiler and Lehman Go Cellular: CW Networks NeurIPS 2021 Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks ICML 2021 Fast geometric learning with symbolic matrices NeurIPS 2020 The Average Mixing Kernel Signature ECCV 2020 Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation ICCV 2019 PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks ICLR 2019 Deformable Shape Completion With Graph Convolutional Autoencoders CVPR 2018 Deep Functional Maps: Structured Prediction for Dense Shape Correspondence ICCV 2017 Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks NeurIPS 2017 Learning shape correspondence with anisotropic convolutional neural networks NeurIPS 2016 Robust Principal Component Analysis on Graphs ICCV 2015