no code implementations • 9 Jan 2024 • Haoyang Chen, Peiyan Sun, Qiyuan Song, Wanyuan Wang, Weiwei Wu, Wencan Zhang, Guanyu Gao, Yan Lyu
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.
no code implementations • 30 Jan 2022 • Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim
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.
no code implementations • 28 Dec 2021 • Wencan Zhang, Brian Y. Lim
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.
no code implementations • ICCV 2021 • Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, Brian Y. Lim
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)
no code implementations • 23 Jan 2021 • Danding Wang, Wencan Zhang, Brian Y. Lim
Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference.
no code implementations • 10 Dec 2020 • Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim
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.