Michael Bronstein
27 papers · 2015–2024 · 9 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer π Academic Marathon (9) π Conference Polyglot (9) π Cross-Pollinator (13)
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Conference Polyglot
(9)
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Academic Marathon
(9)
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Cross-Pollinator
(13)
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The Namer
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Deep Specialist
(15)
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Keyword Champion
(2)
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Grand Slam
ποΈ
Keyword Collector
(126)
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Century Club
(27)
π₯
Unstoppable
(10)
β‘
Prolific Year
(6)
Conferences
NIPS (14)
ICML (4)
ICCV (3)
AAAI (1)
CVPR (1)
ECCV (1)
ICLR (1)
JMLR (1)
UAI (1)
Top co-authors
Keywords
graph neural network
(11)
message passing
(4)
flow matching
(3)
graph learning
(3)
weisfeiler-lehman test
(3)
neural diffusion
(2)
simplicial complex
(2)
topological data analysis
(2)
node classification
(2)
graph convolutional network
(2)
geometric deep learning
(2)
graph regularization
(2)
generative model
(2)
sample complexity
(1)
model evaluation
(1)
feature learning
(1)
graph representation learning
(1)
optimal transport
(1)
link prediction
(1)
transformer architecture
(1)
Papers
To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models
UAI 2024
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation
NIPS 2024
Learning on Large Graphs using Intersecting Communities
NIPS 2024
Metric Flow Matching for Smooth Interpolations on the Data Manifold
NIPS 2024
Fisher Flow Matching for Generative Modeling over Discrete Data
NIPS 2024
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
NIPS 2023
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
AAAI 2023
Curvature Filtrations for Graph Generative Model Evaluation
NIPS 2023
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
NIPS 2022
Graph-Coupled Oscillator Networks
ICML 2022
Learning to Infer Structures of Network Games
ICML 2022
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
NIPS 2022
Partition and Code: learning how to compress graphs
NIPS 2021
Transferability of Spectral Graph Convolutional Neural Networks
JMLR 2021
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
ICML 2021
GRAND: Graph Neural Diffusion
ICML 2021
Beltrami Flow and Neural Diffusion on Graphs
NIPS 2021
Weisfeiler and Lehman Go Cellular: CW Networks
NIPS 2021
The Average Mixing Kernel Signature
ECCV 2020
Fast geometric learning with symbolic matrices
NIPS 2020
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
ICLR 2019
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
ICCV 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
NIPS 2017
Learning shape correspondence with anisotropic convolutional neural networks
NIPS 2016
Robust Principal Component Analysis on Graphs
ICCV 2015