18 citations · 32 across the 5 of their papers we have counts for
7 papers
Less Uniform Discrete Diffusion is More Powerful and Scalable
Kaibo Wang, Ding Ding, Fangyu Ding +5
Although uniform diffusion language models (UDLMs) represent a promising diffusion paradigm, scaling them remains challenging. We identify the core obstacle as an over-uniform trai…
Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
Fangyu Ding, Ding Ding, Sijin Chen +8
While Masked Diffusion Language Models (MDLMs) relying on token masking and unmasking have shown promise in language modeling, their computational efficiency and generation flexibi…
Masked Diffusion Models as Energy Minimization
Sitong Chen, Shen Nie, Jiacheng Sun +4
We present a systematic theoretical framework that interprets masked diffusion models (MDMs) as solutions to energy minimization problems in discrete optimal transport. Specificall…
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
Jingyang Ou, Shen Nie, Kaiwen Xue +4
Discrete diffusion models with absorbing processes have shown promise in language modeling. The key quantities to be estimated are the ratios between the marginal probabilities of…
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
Zebin You, Yong Zhong, Fan Bao +3
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built up…
Why Are Conditional Generative Models Better Than Unconditional Ones?
Fan Bao, Chongxuan Li, Jiacheng Sun +1
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do…
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jiacheng Sun +2
Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much…