no code implementations • 5 Sep 2022 • Derek Everett, Andre T. Nguyen, Luke E. Richards, Edward Raff
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.
no code implementations • 18 Feb 2022 • Andre T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
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.
no code implementations • 9 Aug 2021 • Andre T. Nguyen, Edward Raff, Charles Nicholas, James Holt
The detection of malware is a critical task for the protection of computing environments.
no code implementations • 1 Sep 2020 • Andre T. Nguyen, Luke E. Richards, Gaoussou Youssouf Kebe, Edward Raff, Kasra Darvish, Frank Ferraro, Cynthia Matuszek
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.
1 code implementation • CVPR 2020 • Arash Rahnama, Andre T. Nguyen, Edward Raff
We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally.
no code implementations • 6 Nov 2019 • Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data.
no code implementations • 10 Oct 2019 • Andre T. Nguyen, Edward Raff, Aaron Sant-Miller
Successful malware attacks on information technology systems can cause millions of dollars in damage, the exposure of sensitive and private information, and the irreversible destruction of data.
no code implementations • 24 Aug 2019 • Andre T. Nguyen, Edward Raff
Recent work has developed Bayesian methods for the automatic statistical analysis and description of single time series as well as of homogeneous sets of time series data.
no code implementations • 17 Jul 2019 • Arash Rahnama, Andre T. Nguyen, Edward Raff
Significant work is being done to develop the math and tools necessary to build provable defenses, or at least bounds, against adversarial attacks of neural networks.
no code implementations • 7 Dec 2018 • Andre T. Nguyen, Edward Raff
Adversarial attacks against neural networks in a regression setting are a critical yet understudied problem.