Search Results for author: Andre T. Nguyen

Found 10 papers, 1 papers with code

Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training

no code implementations5 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.

Computational Efficiency Out-of-Distribution Detection +1

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

no code implementations18 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.

Classification Image Classification +1

Practical Cross-modal Manifold Alignment for Grounded Language

no code implementations1 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.

Grounded language learning

Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory

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.

Robust Design

Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions

no code implementations6 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.

BIG-bench Machine Learning

Would a File by Any Other Name Seem as Malicious?

no code implementations10 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.

Malware Detection

Heterogeneous Relational Kernel Learning

no code implementations24 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.

Anomaly Detection Clustering +2

Connecting Lyapunov Control Theory to Adversarial Attacks

no code implementations17 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.

Math

Adversarial Attacks, Regression, and Numerical Stability Regularization

no code implementations7 Dec 2018 Andre T. Nguyen, Edward Raff

Adversarial attacks against neural networks in a regression setting are a critical yet understudied problem.

regression

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