2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Discrete Stochastic Localization for Non-autoregressive Generation
Yunshu Wu, Jiayi Cheng, Longxuan Yu +4
Continuous diffusion is a natural framework for non-autoregressive generation but has generally lagged behind masked discrete diffusion models (MDMs) on discrete sequence generatio…
Spectral Regularization for Diffusion Models
Satish Chandran, Nicolas Roque dos Santos, Yunshu Wu +2
Diffusion models are typically trained using pointwise reconstruction objectives that are agnostic to the spectral and multi-scale structure of natural signals. We propose a loss-l…
Discrete Stochastic Localization for Non-autoregressive Generation
Yunshu Wu, Jiayi Cheng, Longxuan Yu +4
Continuous diffusion is a natural framework for non-autoregressive generation but has generally lagged behind masked discrete diffusion models (MDMs) on discrete sequence generatio…
Measurement-Aligned Sampling for Inverse Problem
Shaorong Zhang, Rob Brekelmans, Yunshu Wu +1
Diffusion models provide a powerful way to incorporate complex prior information for solving inverse problems. However, existing methods struggle to correctly incorporate guidance…
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
Yunshu Wu, Yingtao Luo, Xianghao Kong +2
Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. In this paper, we revisit the diffusion sampling pr…