7 citations · 28 across the 19 of their papers we have counts for
16 papers · 1 filter
Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
Jaeyeon Kim, Jonathan Geuter, David Alvarez-Melis +2
Masked Diffusion Models (MDMs) have emerged as a promising approach for generative modeling in discrete spaces. By generating sequences in any order and allowing for parallel decod…
Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee +4
A natural desideratum for generative models is self-correction--detecting and revising low-quality tokens at inference. While Masked Diffusion Models (MDMs) have emerged as a promi…
Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel
Shivam Gupta, Linda Cai, Sitan Chen
Sampling algorithms play an important role in controlling the quality and runtime of diffusion model inference. In recent years, a number of works~\cite{chen2023sampling,chen2023od…
Critical windows: non-asymptotic theory for feature emergence in diffusion models
Marvin Li, Sitan Chen
We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows. Empirically, it has been observed that there are narr…
A faster and simpler algorithm for learning shallow networks
Sitan Chen, Shyam Narayanan
We revisit the well-studied problem of learning a linear combination of ReLU activations given labeled examples drawn from the standard -dimensional Gaussian measure. Chen e…
The probability flow ODE is provably fast
Sitan Chen, Sinho Chewi, Holden Lee +3
We provide the first polynomial-time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling. Our ana…