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Found 6 papers, 0 papers with code
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order.
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning
We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions.
Towards Relatable Explainable AI with the Perceptual Process
Inspired by the perceptual process from cognitive psychology, we propose the XAI Perceptual Processing Framework and RexNet model for relatable explainable AI with Contrastive Saliency, Counterfactual Synthetic, and Contrastive Cues explanations.
Exploiting Explanations for Model Inversion Attacks
We study this risk for image-based model inversion attacks and identified several attack architectures with increasing performance to reconstruct private image data from model explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations
Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference.
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions.
