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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.
AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding
Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often characterized by distinct feature sets or descriptive metadata.
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.
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks
The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications.
