Matthias Fey

Orcid: 0000-0002-5727-0701

According to our database1, Matthias Fey authored at least 25 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
RelBench: A Benchmark for Deep Learning on Relational Databases.
CoRR, 2024

From Similarity to Superiority: Channel Clustering for Time Series Forecasting.
CoRR, 2024

PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning.
CoRR, 2024

FASTEN: Fast GPU-accelerated Segmented Matrix Multiplication for Heterogenous Graph Neural Networks.
Proceedings of the 38th ACM International Conference on Supercomputing, 2024

Position: Relational Deep Learning - Graph Representation Learning on Relational Databases.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Weisfeiler and Leman go Machine Learning: The Story so far.
J. Mach. Learn. Res., 2023

Relational Deep Learning: Graph Representation Learning on Relational Databases.
CoRR, 2023

Temporal Graph Benchmark for Machine Learning on Temporal Graphs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Expressives und Effizientes Deep Learning auf Graph-Strukturierten Daten.
Ausgezeichnete Informatikdissertationen, 2022

On the power of message passing for learning on graph-structured data.
PhD thesis, 2022

Deep Graph Representation Learning.
Proceedings of the Machine Learning under Resource Constraints - Volume 1: Fundamentals, 2022

2021
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Hierarchical Inter-Message Passing for Learning on Molecular Graphs.
CoRR, 2020

Open Graph Benchmark: Datasets for Machine Learning on Graphs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Graph Matching Consensus.
Proceedings of the 8th International Conference on Learning Representations, 2020

Adversarial Generation of Continuous Implicit Shape Representations.
Proceedings of the 41st Annual Conference of the European Association for Computer Graphics, 2020

2019
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks.
CoRR, 2019

Fast Graph Representation Learning with PyTorch Geometric.
CoRR, 2019

Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Recognizing Cuneiform Signs Using Graph Based Methods.
Proceedings of the International Workshop on Cost-Sensitive Learning, 2018

Group Equivariant Capsule Networks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

SplineCNN: Fast Geometric Deep Learning With Continuous B-Spline Kernels.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2016
CP- and OCF-networks - a comparison.
Fuzzy Sets Syst., 2016


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