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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…
si4onnx: A Python package for Selective Inference in Deep Learning Models
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
In this paper, we introduce si4onnx, a package for performing selective inference on deep learning models. Techniques such as CAM in XAI and reconstruction-based anomaly detection…