Matthias Fey

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

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

RelBench: A Benchmark for Deep Learning on Relational Databases.

Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec

CoRR, 2024

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

Jialin Chen, Jan Eric Lenssen, Aosong Feng, Weihua Hu, Matthias Fey, Leandros Tassiulas, Jure Leskovec, Rex Ying

CoRR, 2024

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

Weihua Hu, Yiwen Yuan, Zecheng Zhang, Akihiro Nitta, Kaidi Cao, Vid Kocijan, Jure Leskovec, Matthias Fey

CoRR, 2024

FASTEN: Fast GPU-accelerated Segmented Matrix Multiplication for Heterogenous Graph Neural Networks.

Keren Zhou, Karthik Ganapathi Subramanian, Po-Hsun Lin, Matthias Fey, Binqian Yin, Jiajia Li

Proceedings of the 38th ACM International Conference on Supercomputing, 2024

Position: Relational Deep Learning - Graph Representation Learning on Relational Databases.

Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Weisfeiler and Leman go Machine Learning: The Story so far.

Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt

J. Mach. Learn. Res., 2023

Relational Deep Learning: Graph Representation Learning on Relational Databases.

Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec

CoRR, 2023

Temporal Graph Benchmark for Machine Learning on Temporal Graphs.

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany

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.

Matthias Fey

Ausgezeichnete Informatikdissertationen, 2022

On the power of message passing for learning on graph-structured data.

Matthias Fey

PhD thesis, 2022

Deep Graph Representation Learning.

Matthias Fey, Frank Weichert

Proceedings of the Machine Learning under Resource Constraints - Volume 1: Fundamentals, 2022

2021

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec

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.

Christopher Morris, Matthias Fey, Nils M. Kriege

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings.

Matthias Fey, Jan Eric Lenssen, Frank Weichert, Jure Leskovec

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Hierarchical Inter-Message Passing for Learning on Molecular Graphs.

Matthias Fey, Jan-Gin Yuen, Frank Weichert

CoRR, 2020

Open Graph Benchmark: Datasets for Machine Learning on Graphs.

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Graph Matching Consensus.

Matthias Fey, Jan Eric Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege

Proceedings of the 8th International Conference on Learning Representations, 2020

Adversarial Generation of Continuous Implicit Shape Representations.

Marian Kleineberg, Matthias Fey, Frank Weichert

Proceedings of the 41st Annual Conference of the European Association for Computer Graphics, 2020

2019

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks.

Matthias Fey

CoRR, 2019

Fast Graph Representation Learning with PyTorch Geometric.

Matthias Fey, Jan Eric Lenssen

CoRR, 2019

Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks.

Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Recognizing Cuneiform Signs Using Graph Based Methods.

Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert

Proceedings of the International Workshop on Cost-Sensitive Learning, 2018

Group Equivariant Capsule Networks.

Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski

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.

Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2016

CP- and OCF-networks - a comparison.

Christian Eichhorn, Matthias Fey, Gabriele Kern-Isberner

Fuzzy Sets Syst., 2016