Search Results for author: Sejin Kim

Found 6 papers, 2 papers with code

Optimal mesh generation for a blade passage using deep reinforcement learning

no code implementations8 Sep 2022 Innyoung Kim, Sejin Kim, Donghyun You

A mesh generation method that can generate an optimal mesh for a blade passage at a single attempt is developed using deep reinforcement learning (DRL).

reinforcement-learning

FastCPH: Efficient Survival Analysis for Neural Networks

2 code implementations21 Aug 2022 Xuelin Yang, Louis Abraham, Sejin Kim, Petr Smirnov, Feng Ruan, Benjamin Haibe-Kains, Robert Tibshirani

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 linear model in its original form.

Survival Analysis

Control of a fly-mimicking flyer in complex flow using deep reinforcement learning

no code implementations4 Nov 2021 Seungpyo Hong, Sejin Kim, Donghyun You

For the deep-RL to successfully learn the control policy, accurate and ample data of the dynamics are required.

reinforcement-learning

Multi-condition multi-objective optimization using deep reinforcement learning

no code implementations10 Oct 2021 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.

reinforcement-learning

A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

no code implementations28 Jan 2021 Michal Kazmierski, Mattea Welch, Sejin Kim, Chris McIntosh, Princess Margaret Head, Neck Cancer Group, Katrina Rey-McIntyre, Shao Hui Huang, Tirth Patel, Tony Tadic, Michael Milosevic, Fei-Fei Liu, Andrew Hope, Scott Bratman, Benjamin Haibe-Kains

We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis.

BIG-bench Machine Learning Survival Prediction

Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks

1 code implementation10 Dec 2020 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.

BIG-bench Machine Learning Survival Prediction

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