181 citations · 1.7k across the 107 of their papers we have counts for
13 papers · 1 filter
Dirichlet belief networks for topic structure learning
He Zhao, Lan Du, Wray Buntine +1
Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed…
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh +2
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS…
Deep Poisson gamma dynamical systems
Dandan Guo, Bo Chen, Hao Zhang +1
We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarc…
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
Quan Zhang, Mingyuan Zhou
We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to…
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
Mingzhang Yin, Mingyuan Zhou
To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computa…
Semi-Implicit Variational Inference
Mingzhang Yin, Mingyuan Zhou
Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible d…
Parsimonious Bayesian deep networks
Mingyuan Zhou
Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architect…
Masking: A New Perspective of Noisy Supervision
Bo Han, Jiangchao Yao, Gang Niu +4
It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth…
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Paul de Figueiredo +3
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms comp…
Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield +2
We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the soc…
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
Hao Zhang, Bo Chen, Dandan Guo +1
To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid aut…
Discussion on "Sparse graphs using exchangeable random measures" by Francois Caron and Emily B. Fox
Mingyuan Zhou
This is a discussion on "Sparse graphs using exchangeable random measures" by Francois Caron and Emily B. Fox, published in Journal of the Royal Statistical Society, Series B, 2017…
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems
Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data. SGLDS uses the Bernoulli-Poisson link together w…