Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Nguyen, Andre"'
In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.
Externí odkaz:
http://arxiv.org/abs/2410.16423
The quantification of uncertainty is important for the adoption of machine learning, especially to reject out-of-distribution (OOD) data back to human experts for review. Yet progress has been slow, as a balance must be struck between computational e
Externí odkaz:
http://arxiv.org/abs/2209.03148
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddings induced by the in
Externí odkaz:
http://arxiv.org/abs/2202.08985
Autor:
Richards, Luke E., Nguyen, André, Capps, Ryan, Forsythe, Steven, Matuszek, Cynthia, Raff, Edward
The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community. The ability to successfully evade unseen models represents an uncomforta
Externí odkaz:
http://arxiv.org/abs/2109.11125
Publikováno v:
Bulletin of the Ecological Society of America, 2023 Jan 01. 104(1), 1-7.
Externí odkaz:
https://www.jstor.org/stable/48708244
The detection of malware is a critical task for the protection of computing environments. This task often requires extremely low false positive rates (FPR) of 0.01% or even lower, for which modern machine learning has no readily available tools. We i
Externí odkaz:
http://arxiv.org/abs/2108.04081
Autor:
Nguyen, Andre T., Richards, Luke E., Kebe, Gaoussou Youssouf, Raff, Edward, Darvish, Kasra, Ferraro, Frank, Matuszek, Cynthia
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of ancho
Externí odkaz:
http://arxiv.org/abs/2009.05147
Autor:
Kogan, Nicole E., Clemente, Leonardo, Liautaud, Parker, Kaashoek, Justin, Link, Nicholas B., Nguyen, Andre T., Lu, Fred S., Huybers, Peter, Resch, Bernd, Havas, Clemens, Petutschnig, Andreas, Davis, Jessica, Chinazzi, Matteo, Mustafa, Backtosch, Hanage, William P., Vespignani, Alessandro, Santillana, Mauricio
Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic wa
Externí odkaz:
http://arxiv.org/abs/2007.00756