2 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
Control of a fly-mimicking flyer in complex flow using deep reinforcement learning
Seungpyo Hong, Sejin Kim, Donghyun You
An integrated framework of computational fluid-structural dynamics (CFD-CSD) and deep reinforcement learning (deep-RL) is developed for control of a fly-scale flexible-winged flyer…
cs.LG2021★ 1 cited
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
Michal Kazmierski, Mattea Welch, Sejin Kim +12
Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider rang…
cs.LG2020★ 2 cited
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
Sejin Kim, Michal Kazmierski, Benjamin Haibe-Kains
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been signific…