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04/27/2023
Detection of Adversarial Physical Attacks in Time-Series Image Data
Deep neural networks (DNN) have become a common sensing modality in auto...
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02/21/2023
Using Semantic Information for Defining and Detecting OOD Inputs
As machine learning models continue to achieve impressive performance ac...
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07/24/2022
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inp...
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06/13/2022
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Adversarial training (AT) and its variants have spearheaded progress in ...
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01/07/2022
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Machine learning methods such as deep neural networks (DNNs), despite th...
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08/13/2021
Detecting OODs as datapoints with High Uncertainty
Deep neural networks (DNNs) are known to produce incorrect predictions w...
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03/23/2021
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Deep neural networks (DNNs) are known to produce incorrect predictions w...
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06/20/2018