2 citations · 2 across the 5 of their papers we have counts for
5 papers
Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
Ding Huang, Ting Li, Jian Huang
We propose a Bayesian framework for fine-tuning large diffusion models with a novel network structure called Bayesian Power Steering (BPS). We clarify the meaning behind adaptation…
Convergence of Continuous Normalizing Flows for Learning Probability Distributions
Yuan Gao, Jian Huang, Yuling Jiao +1
Continuous normalizing flows (CNFs) are a generative method for learning probability distributions, which is based on ordinary differential equations. This method has shown remarka…
XMM-Newton Observations of Two Archival X-ray Weak Type 1 Quasars: Obscuration Induced X-ray Weakness and Variability
Zijian Zhang, Bin Luo, W. N. Brandt +6
We report \hbox{XMM-Newton} observations of two examples of an unclassified type of \hbox{X-ray} weak quasars from the \citet{2020ApJ...900..141P} survey of \hbox{X-ray} weak quasa…
Deep Sufficient Representation Learning via Mutual Information
Siming Zheng, Yuanyuan Lin, Jian Huang
We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approxi…
Wasserstein Generative Learning of Conditional Distribution
Shiao Liu, Xingyu Zhou, Yuling Jiao +1
Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a co…