NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:1742
Title:Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

The reviewers all reached consensus to accept this work --- congratulations! Some requests in the revision: ** address redundancy in the Cost Matrix and C_node ** improve clarity, in particular the missing discussion of the node measure ** make sure that reading this paper does not require detailed knowledge of [48]