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Found 4 papers, 1 papers with code
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection.
A Human-Grounded Evaluation of SHAP for Alert Processing
In this paper we present the results of a human-grounded evaluation of SHAP, an explanation method that has been well-received in the XAI and related communities.
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted.
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification.
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