34 citations · 80 across the 18 of their papers we have counts for
35 papers
Self-Verification in Image Denoising
Huangxing Lin, Yihong Zhuang, Delu Zeng +3
We devise a new regularization, called self-verification, for image denoising. This regularization is formulated using a deep image prior learned by the network, rather than a trad…
Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures
Elizabeth A. Gibson, Sebastian T. Rowland, Jeff Goldsmith +3
Environmental health researchers may aim to identify exposure patterns that represent sources, product use, or behaviors that give rise to mixtures of potentially harmful environme…
Adaptive noise imitation for image denoising
Huangxing Lin, Yihong Zhuang, Yue Huang +4
The effectiveness of existing denoising algorithms typically relies on accurate pre-defined noise statistics or plenty of paired data, which limits their practicality. In this work…
Bayesian recurrent state space model for rs-fMRI
Arunesh Mittal, Scott Linderman, John Paisley +1
We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network…
Deep Bayesian Nonparametric Factor Analysis
Arunesh Mittal, Paul Sajda, John Paisley
We propose a deep generative factor analysis model with beta process prior that can approximate complex non-factorial distributions over the latent codes. We outline a stochastic E…
Learning Rate Dropout
Huangxing Lin, Weihong Zeng, Xinghao Ding +3
The performance of a deep neural network is highly dependent on its training, and finding better local optimal solutions is the goal of many optimization algorithms. However, exist…