12 citations · 13 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning Kronecker-Structured Graphs from Smooth Signals
Changhao Shi, Gal Mishne
Graph learning, or network inference, is a prominent problem in graph signal processing (GSP). GSP generalizes the Fourier transform to non-Euclidean domains, and graph learning is…
Learning Cartesian Product Graphs with Laplacian Constraints
Changhao Shi, Gal Mishne
Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amount…
Graph Laplacian Learning with Exponential Family Noise
Changhao Shi, Gal Mishne
Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matr…
Online Adversarial Purification based on Self-Supervision
Changhao Shi, Chester Holtz, Gal Mishne
Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. I…