45 citations · 54 across the 6 of their papers we have counts for
6 papers
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning
Innyoung Kim, Sejin Kim, Donghyun You
A method using deep reinforcement learning (DRL) to non-iteratively generate an optimal mesh for an arbitrary blade passage is developed. Despite automation in mesh generation usin…
FastCPH: Efficient Survival Analysis for Neural Networks
Xuelin Yang, Louis Abraham, Sejin Kim +4
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a lin…
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…
Multi-condition multi-objective optimization using deep reinforcement learning
Sejin Kim, Innyoung Kim, Donghyun You
A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. U…
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…
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…