Search Results for author:
Found 8 papers, 2 papers with code
CohEx: A Generalized Framework for Cohort Explanation
eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions.
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
Time series anomaly detection is a critical machine learning task for numerous applications, such as finance, healthcare, and industrial systems.
Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing Precision in Agriculture
Fruit distribution is pivotal in shaping the future of both agriculture and agricultural robotics, paving the way for a streamlined supply chain.
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective
Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approaches as a leading family.
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Explainability plays an increasingly important role in machine learning.
Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network
Machine learning-based methods have achieved successful applications in machinery fault diagnosis.
Q-Learning for Robust Satisfaction of Signal Temporal Logic Specifications
This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics.
Systems and Control
