1 citations · 1 across the 5 of their papers we have counts for
6 papers
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
Shay Deutsch, Lionel Yelibi, Alex Tong Lin +1
Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a fr…
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…
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,…
Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2
Jeffrey N. Law, Kyle Akers, Nure Tasnina +8
Background: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has bee…
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…
Graph-Based Manifold Frequency Analysis for Denoising
Shay Deutsch, Antonio Ortega, Gerard Medioni
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian…