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cs.LG2024
NeuroSteiner: A Graph Transformer for Wirelength Estimation
Sahil Manchanda, Dana Kianfar, Markus Peschl +2
A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas. Computing the minimal WL of a placement requires finding rectilinear St…
cs.LG2024
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Rishi Shah, Krishnanshu Jain, Sahil Manchanda +2
Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhib…
cs.LG2024
Mirage: Model-Agnostic Graph Distillation for Graph Classification
Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana +1
GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource e…