4 papers · 1 filter
Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference
Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka +1
Graph Neural Networks (GNNs) have gained prominence for their ability to process graph-structured data across various domains. However, interpreting GNN decisions remains a signifi…
Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
Mizuki Niihori, Shuichi Nishino, Teruyuki Katsuoka +3
In real-world applications, anomaly detection (AD) often operates without access to anomalous data, necessitating semi-supervised methods that rely solely on normal data. Among the…
Change Point Detection in the Frequency Domain with Statistical Reliability
Akifumi Yamada, Tomohiro Shiraishi, Shuichi Nishino +3
Effective condition monitoring in complex systems requires identifying change points (CPs) in the frequency domain, as the structural changes often arise across multiple frequencie…
Selective Inference for Changepoint detection by Recurrent Neural Network
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN). Thanks to its…