일시: 2024년 3월 19일(화), 14:00~16:00
장소: 판교 테크노밸리 산업수학혁신센터 세미나실
발표자: 임성빈 교수(고려대학교)
주요내용: Recent Advances in Score-based Generative Models
Diffusion models have recently acquired significant attention in the field of generative modeling of machine learning research due to their various theoretical advantages and remarkable applications in artificial intelligence, such as Stable Diffusion and DALL-E. In this presentation, we first introduce the theoretical background of the diffusion models and score-based diffusion models and present the latest results of their applications to machine learning. We also present advanced score-based generative models based on the time reversal theory of diffusion processes in Hilbert space.
일시: 2024년 3월 19일(화), 14:00~16:00
장소: 판교 테크노밸리 산업수학혁신센터 세미나실
발표자: 임성빈 교수(고려대학교)
주요내용: Recent Advances in Score-based Generative Models
Diffusion models have recently acquired significant attention in the field of generative modeling of machine learning research due to their various theoretical advantages and remarkable applications in artificial intelligence, such as Stable Diffusion and DALL-E. In this presentation, we first introduce the theoretical background of the diffusion models and score-based diffusion models and present the latest results of their applications to machine learning. We also present advanced score-based generative models based on the time reversal theory of diffusion processes in Hilbert space.