2 citations · 2 across the 1 of their papers we have counts for
2 papers
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★ 2 cited
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
Bohang Zhang, Jingchu Gai, Yiheng Du +3
Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph learning community. So far, GNN expressiveness has been primarily assessed via the Weisfeiler-…