Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations

23 January 2021

Papers citing "Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations"

7 / 7 papers shown

Title
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do 24 2 0 11 Dec 2023
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap Q. V. Liao J. Vaughan 36 158 0 02 Jun 2023
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 17 1 0 30 Jan 2022
Towards Relatable Explainable AI with the Perceptual Process Wencan Zhang Brian Y. Lim AAML XAI 20 61 0 28 Dec 2021
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 16 18 0 10 Dec 2020
Attributional Robustness Training using Input-Gradient Spatial Alignment M. Singh Nupur Kumari Puneet Mangla Abhishek Sinha V. Balasubramanian Balaji Krishnamurthy OOD 21 10 0 29 Nov 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 227 3,681 0 28 Feb 2017