Search Results for author:
Found 6 papers, 1 papers with code
Graph Spectral Embedding using the Geodesic Betweeness Centrality
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure.
Spectral Embedding of Graph Networks
We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure.
Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2
Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e. g., determining how much any experimental observation in the input contributes to the score of every prediction.
Zero Shot Learning with the Isoperimetric Loss
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 unknown classes in a zero-shot learning setting.
Zero Shot Learning via Multi-Scale Manifold Regularization
We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs.
Graph-Based Manifold Frequency Analysis for Denoising
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.
