6 citations · 7 across the 15 of their papers we have counts for
5 papers · 1 filter
Statistical Test for Auto Feature Engineering by Selective Inference
Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino +2
Auto Feature Engineering (AFE) plays a crucial role in developing practical machine learning pipelines by automating the transformation of raw data into meaningful features that en…
Statistical Test for Feature Selection Pipelines by Selective Inference
Tomohiro Shiraishi, Tatsuya Matsukawa, Shuichi Nishino +1
A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating various analysis algorithms. In this paper, we propose a…
Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent tr…
Statistical Test for Anomaly Detections by Variational Auto-Encoders
Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy +2
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in…
Statistical Test for Attention Map in Vision Transformer
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka +3
The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among…