3 papers
cs.LG2025
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
Jingchu Gai, Yiheng Du, Bohang Zhang +2
Graph spectra are an important class of structural features on graphs that have shown promising results in enhancing Graph Neural Networks (GNNs). Despite their widespread practica…
cs.LG2024
Do Efficient Transformers Really Save Computation?
Kai Yang, Jan Ackermann, Zhenyu He +6
As transformer-based language models are trained on increasingly large datasets and with vast numbers of parameters, finding more efficient alternatives to the standard Transformer…
cs.LG2024
On the Expressive Power of Spectral Invariant Graph Neural Networks
Bohang Zhang, Lingxiao Zhao, Haggai Maron
Incorporating spectral information to enhance Graph Neural Networks (GNNs) has shown promising results but raises a fundamental challenge due to the inherent ambiguity of eigenvect…