Poster "expressive power" Papers
12 papers found
Conference
A Hierarchy of Graphical Models for Counterfactual Inferences
Hongshuo Yang, Elias Bareinboim
NEURIPS 2025
A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
Will Merrill, Ashish Sabharwal
NEURIPS 2025arXiv:2503.03961
33
citations
The Computational Complexity of Counting Linear Regions in ReLU Neural Networks
Moritz Stargalla, Christoph Hertrich, Daniel Reichman
NEURIPS 2025arXiv:2505.16716
2
citations
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
Yam Eitan, Yoav Gelberg, Guy Bar-Shalom et al.
ICLR 2025arXiv:2408.05486
11
citations
Towards Explaining the Power of Constant-depth Graph Neural Networks for Structured Linear Programming
Qian Li, Minghui Ouyang, Tian Ding et al.
ICLR 2025
1
citations
Aligning Transformers with Weisfeiler-Leman
Luis Müller, Christopher Morris
ICML 2024arXiv:2406.03148
6
citations
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, Ismail Ceylan et al.
ICML 2024arXiv:2402.08595
20
citations
On dimensionality of feature vectors in MPNNs
César Bravo, Alexander Kozachinskiy, Cristobal Rojas
ICML 2024arXiv:2402.03966
8
citations
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
Felix Draxler, Stefan Wahl, Christoph Schnörr et al.
ICML 2024arXiv:2402.06578
17
citations
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
ICML 2024
The Expressive Power of Path-Based Graph Neural Networks
Caterina Graziani, Tamara Drucks, Fabian Jogl et al.
ICML 2024
Weisfeiler-Leman at the margin: When more expressivity matters
Billy Franks, Christopher Morris, Ameya Velingker et al.
ICML 2024arXiv:2402.07568
15
citations