VAN-AD adapts a pretrained visual MAE with distribution mapping and normalizing flow modules to detect anomalies in time series data more effectively across different datasets.
Lof: identifying density-based local outliers
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Supervised models using 83 metrics achieve 0.85-0.9 recall for post-release Python faults, outperforming LLMs, with process metrics and code size most predictive and metrics plus embeddings capturing complementary information.
citing papers explorer
-
VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
VAN-AD adapts a pretrained visual MAE with distribution mapping and normalizing flow modules to detect anomalies in time series data more effectively across different datasets.
-
Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems
Supervised models using 83 metrics achieve 0.85-0.9 recall for post-release Python faults, outperforming LLMs, with process metrics and code size most predictive and metrics plus embeddings capturing complementary information.