3 citations · 8 across the 10 of their papers we have counts for
4 papers · 1 filter
Supervised Contrastive Block Disentanglement
Taro Makino, Ji Won Park, Natasa Tagasovska +11
Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it diffic…
Generative multitask learning mitigates target-causing confounding
Taro Makino, Krzysztof J. Geras, Kyunghyun Cho
We propose generative multitask learning (GMTL), a simple and scalable approach to causal representation learning for multitask learning. Our approach makes a minor change to the c…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Understanding the robustness of deep neural network classifiers for breast cancer screening
Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski +5
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented…