N. Piatkowski, K. Morik, N. Kriege, C. Morris, M. Fey, F. Weichert, N. Bertram, J. Ellert, J. Fischer and L. Pfahler, "Structured Data" in Machine Learning under Resource Constraints - Fundamentals, De Gruyter, 2023, pp. 99--178.

doi:10.1515/9783110785944-004

M. Fey, "On the power of message passing for learning on graph-structured data", Dissertation, Universität Dortmund, 2022.

Link

M. Fey, J. E. Lenssen, F. Weichert and J. Leskovec, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" , International Conference on Machine Learning (ICML), 2021.

Link

M. Kleineberg, M. Fey and F. Weichert, "Adversarial Generation of Continuous Implicit Shape Representations" in Eurographics, 2020.

PDF

Link

M. Fey, J. E. Lenssen, C. Morris, J. Masci and N. Kriege, "Deep Graph Matching Consensus" in International Conference on Learning Representations (ICLR), 2020.

PDF

Link

M. Fey, J. Yuen and F. Weichert, "Hierarchical Inter-Message Passing for Learning on Molecular Graphs" in ICML Graph Representation Learning and Beyond (GRL+), 2020.

PDF

Link

M. Fey and J. E. Lenssen, "Fast Graph Representation Learning with PyTorch Geometric" in ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.

Datei

M. Fey, "Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks" in ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.

Datei

C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan and M. Grohe, "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks" in 33rd AAAI Conference on Artificial Intelligence (AAAI-19), 2019.

Datei

J. E. Lenssen, M. Fey and P. Libuschewski, "Group Equivariant Capsule Networks" in Neural Information Processing Systems (NIPS) 32, 2018.

Datei

N. Kriege, M. Fey, D. Fisseler, P. Mutzel and F. Weichert, "Recognizing Cuneiform Signs Using Graph Based Methods" in International Workshop on Cost-Sensitive Learning (COST), 2018.

Datei

M. Fey, J. E. Lenssen, F. Weichert and H. Müller, "SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. pp. 869-877.

Datei

H. Müller and M. Otte, "Solving algebraic systems in Bernstein-Bezier repräsentation" in Computional Geometry, Lecture Notes in Computer Science, Springer-Verlag, 1991, pp. 161-169.

H. Müller, J. Winckler, S. Grzybek, M. Otte, B. Stoll, F. Equoy and N. Higelin, "The program animation system PASTIS" in The Journal of Visualization and Computer Animation 2, Springer-Verlag, 1991, pp. 26-33.