Chieh-Hsin
Lai

Profile

Since 2021, Jesse has worked as a research scientist at Sony AI, focusing on robustness, deep generative models, and theoretical deep learning. Prior to Sony AI, he worked as a Research Assistant at the Institute of Mathematics Academia Sinica. Jesse earned his PhD in Mathematics from the University of Minnesota - Twin Cities.

Message

“I specialize in developing theoretical grounded deep generative models that excel in producing high-fidelity samples, rapid sampling, ease of training, and controllable generation. I expect to unlock the black-box nature of deep generative modeling through the application of advanced mathematical tools. With a focus on innovation and precision, I am dedicated to pushing the boundaries of artificial intelligence and contributing to the advancement of cutting-edge technology.”

Publications

VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance

ICASSP, 2023
Carlos Hernandez-Olivan*, Koichi Saito, Naoki Murata, Chieh-Hsin Lai, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Yuki Mitsufuji

Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation. Recent diffusion-based music restoration methods have demonstrated impressive performance, and among them, diffusion posterior sampling (DPS) stands out given its intrin…

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

ICML, 2023
Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji

Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we prop…

FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation

ICML, 2023
Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon*

Score-based generative models learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise. These perturbed data densities are tied together by the Fokker-Planck equation (FPE), a partial differentia…

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