9 citations · 26 across the 5 of their papers we have counts for
4 papers · 1 filter
DE-HNN: An effective neural model for Circuit Netlist representation
Zhishang Luo, Truong Son Hy, Puoya Tabaghi +7
The run-time for optimization tools used in chip design has grown with the complexity of designs to the point where it can take several days to go through one design cycle which ha…
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
Cai Zhou, Rose Yu, Yusu Wang
Graph transformers have recently received significant attention in graph learning, partly due to their ability to capture more global interaction via self-attention. Nevertheless,…
Universal Representation of Permutation-Invariant Functions on Vectors and Tensors
Puoya Tabaghi, Yusu Wang
A main object of our study is multiset functions -- that is, permutation-invariant functions over inputs of varying sizes. Deep Sets, proposed by \cite{zaheer2017deep}, provides a…
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Mitchell Black, Zhengchao Wan, Amir Nayyeri +1
Message passing graph neural networks (GNNs) are a popular learning architectures for graph-structured data. However, one problem GNNs experience is oversquashing, where a GNN has…