Zhenghan Fang

2 accepted papers

2025

Beyond Scores: Proximal Diffusion Models

Zhenghan Fang, Mateo Diaz Diaz, Sam Buchanan, Jeremias Sulam

NeurIPS 2025poster

Diffusion models have quickly become some of the most popular and powerful generative models for high-dimensional data. The key insight that enabled their development was the realization that access to the score---the gradient of the log-density at different noise levels---allows for sampling from d…

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2024

What's in a Prior? Learned Proximal Networks for Inverse Problems

Zhenghan Fang, Sam Buchanan, Jeremias Sulam

ICLR 2024poster

Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep learning models have been brought to bear for these tasks too, as in the framework of plug-and-play or deep unrolling, where th…

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