2 citations · 2 across the 4 of their papers we have counts for
4 papers
Tensor Product Neural Networks for Functional ANOVA Model
Seokhun Park, Insung Kong, Yongchan Choi +2
Interpretability for machine learning models is becoming more and more important as machine learning models become more complex. The functional ANOVA model, which decomposes a high…
META-ANOVA: Screening interactions for interpretable machine learning
Yongchan Choi, Seokhun Park, Chanmoo Park +2
There are two things to be considered when we evaluate predictive models. One is prediction accuracy,and the other is interpretability. Over the recent decades, many prediction mod…
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
Dongha Kim, Yongchan Choi, Kunwoong Kim +1
In many classification problems, collecting massive clean-annotated data is not easy, and thus a lot of researches have been done to handle data with noisy labels. Most recent stat…
Understanding and Improving Virtual Adversarial Training
Dongha Kim, Yongchan Choi, Yongdai Kim
In semi-supervised learning, virtual adversarial training (VAT) approach is one of the most attractive method due to its intuitional simplicity and powerful performances. VAT finds…