Virtual Adversarial Training

Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives
Ph.D. dissertation
Slides DOI Bulletin
Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
Springer Applied Intelligence (Impact Factor: 5.019 in 2021)
PDF DOI Preprint
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training for Semi-Supervised Text Classification
Preprint version of the Springer Applied Intelligence paper “Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training”
DOI Preprint
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training for Semi-Supervised Text Classification
半教師あり文書分類のための仮想敵対的学習による注意機構の頑健性および解釈性の向上
言語処理学会第 27 回年次大会，2021.
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半教師あり文書分類のための仮想敵対的学習による注意機構の頑健性および解釈性の向上