3 papers
cs.LG2022
Graph Spectral Embedding using the Geodesic Betweeness Centrality
Shay Deutsch, Stefano Soatto
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvest…
cs.LG2020
Spectral Embedding of Graph Networks
Shay Deutsch, Stefano Soatto
We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian,…
cs.LG2019
Zero Shot Learning with the Isoperimetric Loss
Shay Deutsch, Andrea Bertozzi, Stefano Soatto
We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unk…