학술행사
- 발표자 최재웅 박사(고등과학원 AI 기초과학센터)
- 개최일시 2024-04-02 14:00-16:00
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일시: 2024년 4월 2일(화), 14:00~16:00
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장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
- 무료주차는 2시간 지원됩니다.
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발표자: 최재웅 박사(고등과학원 AI 기초과학센터)
-
주요내용: Generative Modeling through Optimal Transport
Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a specified cost function. OT theory has been widely utilized in generative modeling. Initially, the OT-based Wasserstein metric served as a measure for assessing the distance between data and generated distributions. More recently, the OT transport map, connecting data and prior distributions, has emerged as a new approach for generative models. In this talk, we will introduce generative models based on Optimal Transport. Specifically, we will present our work on a generative model utilizing Unbalanced Optimal Transport. We will also discuss our subsequent efforts to address the challenges associated with this approach.
-
일시: 2024년 4월 2일(화), 14:00~16:00
-
장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
- 무료주차는 2시간 지원됩니다.
-
발표자: 최재웅 박사(고등과학원 AI 기초과학센터)
-
주요내용: Generative Modeling through Optimal Transport
Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a specified cost function. OT theory has been widely utilized in generative modeling. Initially, the OT-based Wasserstein metric served as a measure for assessing the distance between data and generated distributions. More recently, the OT transport map, connecting data and prior distributions, has emerged as a new approach for generative models. In this talk, we will introduce generative models based on Optimal Transport. Specifically, we will present our work on a generative model utilizing Unbalanced Optimal Transport. We will also discuss our subsequent efforts to address the challenges associated with this approach.
