1 code implementation • 8 Aug 2023 • Harry Li, Steven Jorgensen, John Holodnak, Allan Wollaber
ScatterUQ leverages recent advances in distance-aware neural networks, together with dimensionality reduction techniques, to construct robust, 2-D scatter plots explaining why a model predicts a test example to be (1) in-distribution and of a particular class, (2) in-distribution but unsure of the class, and (3) out-of-distribution.
no code implementations • 2 Jun 2023 • Giorgio Severi, Simona Boboila, Alina Oprea, John Holodnak, Kendra Kratkiewicz, Jason Matterer
As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical.
no code implementations • 11 May 2022 • Steven Jorgensen, John Holodnak, Jensen Dempsey, Karla de Souza, Ananditha Raghunath, Vernon Rivet, Noah DeMoes, Andrés Alejos, Allan Wollaber
We also present an ML framework that is designed to rapidly train with modest data requirements and provide both calibrated, predictive probabilities as well as an interpretable "out-of-distribution" (OOD) score to flag novel traffic samples.