37 citations · 68 across the 11 of their papers we have counts for
4 papers · 1 filter
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks
Isay Katsman, Ethan Lou, Anna Gilbert
Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in…
Shedding Light on Problems with Hyperbolic Graph Learning
Isay Katsman, Anna Gilbert
Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance…
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Qian Huang, Isay Katsman, Horace He +3
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…
Intermediate Level Adversarial Attack for Enhanced Transferability
Qian Huang, Zeqi Gu, Isay Katsman +5
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…