1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Implet: A Post-hoc Subsequence Explainer for Time Series Models
Fanyu Meng, Ziwen Kan, Shahbaz Rezaei +3
Explainability in time series models is crucial for fostering trust, facilitating debugging, and ensuring interpretability in real-world applications. In this work, we introduce Im…
cs.LG2024
Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
Fanyu Meng, Jules Larke, Xin Liu +4
Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to…
cs.LG2024
CohEx: A Generalized Framework for Cohort Explanation
Fanyu Meng, Xin Liu, Zhaodan Kong +1
eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing e…