126 citations · 376 across the 37 of their papers we have counts for
9 papers · 1 filter
Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling
Huangjie Zheng, Shansan Gong, Ruixiang Zhang +5
Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an 'information void' where semantic informati…
TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Tianrong Chen, Huangjie Zheng, David Berthelot +3
Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise schedul…
CARD: Classification and Regression Diffusion Models
Xizewen Han, Huangjie Zheng, Mingyuan Zhou
Learning the distribution of a continuous or categorical response variable given its covariates is a fundamental problem in statistics and machine l…
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning
Shentao Yang, Zhendong Wang, Huangjie Zheng +2
Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy tra…
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
Huangjie Zheng, Pengcheng He, Weizhu Chen +1
Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffu…
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions
Huangjie Zheng, Mingyuan Zhou
To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes' theorem to construct…
Node Attribute Generation on Graphs
Xu Chen, Siheng Chen, Huangjie Zheng +4
Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised…
Understanding VAEs in Fisher-Shannon Plane
Huangjie Zheng, Jiangchao Yao, Ya Zhang +2
In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the unce…
Degeneration in VAE: in the Light of Fisher Information Loss
Huangjie Zheng, Jiangchao Yao, Ya Zhang +1
While enormous progress has been made to Variational Autoencoder (VAE) in recent years, similar to other deep networks, VAE with deep networks suffers from the problem of degenerat…