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On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle.
Learning to Detect Adversarial Examples Based on Class Scores
Our method is able to detect adversarial examples generated by various attacks, and can be easily adopted to a plethora of deep classification models.
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
This constitutes a statistical inference task in terms of the optimal latent space activations of the underlying uncorrupted datum.
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