"expressive power" Papers
16 papers found
Conference
A Hierarchy of Graphical Models for Counterfactual Inferences
Hongshuo Yang, Elias Bareinboim
A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
Will Merrill, Ashish Sabharwal
Bridging Theory and Practice in Link Representation with Graph Neural Networks
Veronica Lachi, Francesco Ferrini, Antonio Longa et al.
Dung’s Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases
Yasir Mahmood, Markus Hecher, Axel-Cyrille Ngonga Ngomo
Learning More Expressive General Policies for Classical Planning Domains
Simon Ståhlberg, Blai Bonet, Hector Geffner
The Computational Complexity of Counting Linear Regions in ReLU Neural Networks
Moritz Stargalla, Christoph Hertrich, Daniel Reichman
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
Yam Eitan, Yoav Gelberg, Guy Bar-Shalom et al.
Towards Explaining the Power of Constant-depth Graph Neural Networks for Structured Linear Programming
Qian Li, Minghui Ouyang, Tian Ding et al.
Aligning Transformers with Weisfeiler-Leman
Luis Müller, Christopher Morris
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, Ismail Ceylan et al.
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Kangkang Lu, Yanhua Yu, Hao Fei et al.
On dimensionality of feature vectors in MPNNs
César Bravo, Alexander Kozachinskiy, Cristobal Rojas
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
Felix Draxler, Stefan Wahl, Christoph Schnörr et al.
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
The Expressive Power of Path-Based Graph Neural Networks
Caterina Graziani, Tamara Drucks, Fabian Jogl et al.
Weisfeiler-Leman at the margin: When more expressivity matters
Billy Franks, Christopher Morris, Ameya Velingker et al.