no code implementations • 27 Feb 2023 • Michael Valancius, Max Lennon, Junier Oliva
We develop novel methodology for active feature acquisition (AFA), the study of how to sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes acquisition costs whilst still yielding accurate predictions.
no code implementations • 16 Aug 2021 • Max Lennon, Nathan Drenkow, Philippe Burlina
To this end, several contributions are made here: A) we develop a new metric called mean Attack Success over Transformations (mAST) to evaluate patch attack robustness and invariance; and B), we systematically assess robustness of patch attacks to 3D position and orientation for various conditions; in particular, we conduct a sensitivity analysis which provides important qualitative insights into attack effectiveness as a function of the 3D pose of a patch relative to the camera (rotation, translation) and sets forth some properties for patch attack 3D invariance; and C), we draw novel qualitative conclusions including: 1) we demonstrate that for some 3D transformations, namely rotation and loom, increasing the training distribution support yields an increase in patch success over the full range at test time.
no code implementations • 1 May 2020 • Neil Fendley, Max Lennon, I-Jeng Wang, Philippe Burlina, Nathan Drenkow
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches.