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Found 6 papers, 1 papers with code
Model Debiasing via Gradient-based Explanation on Representation
Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem.
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models
We examined whether embedding human attention knowledge into saliency-based explainable AI (XAI) methods for computer vision models could enhance their plausibility and faithfulness.
Sensor Data Validation and Driving Safety in Autonomous Driving Systems
In this thesis, we study the detection methods against the attacks on onboard sensors and the linkage between attacked deep learning models and driving safety for autonomous vehicles.
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question.
Ranked #1 on
Question Answering
on KILT: ELI5
Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems
In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method.
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
In this paper, we investigate the impact of two primary types of adversarial attacks, perturbation attacks and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models.
