RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

no code implementations • 28 May 2018 • Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, Jaegul Choo

Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

34 code implementations • CVPR 2018 • Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, Jaegul Choo

To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.

Attribute Image-to-Image Translation +1

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

1 code implementation • 7 Mar 2017 • Min-Je Choi, Sehun Jeong, Hakjoo Oh, Jaegul Choo

Our experimental results using source codes demonstrate that our proposed model is capable of accurately detecting simple buffer overruns.

Question Answering