Mingjun Zhong

发表

The Mathematical Principles of AFNI and One of its Applications to the Research of the Functional Neuroimages

Mingjun Zhong, Huanwen Tang, Jinghai Feng, 2002 .

Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization

Mingjun Zhong, Junfu Du, Mingjun Zhong, 2017, Neural Processing Letters.

A Simple and Flexible Nonlinearty Approach to Independent Component Analysis

Mingjun Zhong, Mingjun Zhong, 2005, 2005 International Conference on Neural Networks and Brain.

Scale-free network model under exogenous pressures

Qiang Guo, Jian-Guo Liu, Mingjun Zhong, 2008, Int. J. Model. Identif. Control..

Sequence-to-point learning with neural networks for nonintrusive load monitoring

Charles A. Sutton, Mingjun Zhong, Nigel H. Goddard, 2016, AAAI.

On the Fully Bayesian Treatment of Latent Gaussian Models using Stochastic Simulations

Maurizio Filippone, Mark Girolami, Mingjun Zhong, 2012 .

Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012

Mark Girolami, Mingjun Zhong, M. Girolami, 2012 .

Transfer Learning for Non-Intrusive Load Monitoring

Stefano Squartini, Mingjun Zhong, Michele DIncecco, 2019, IEEE Transactions on Smart Grid.

Advances in Neural Information Processing Systems 28 (NIPS 2015)

Mingjun Zhong, Charles Sutton, Nigel Goddard, 2014 .

An EM Algorithm for Independent Component Analysis in the Presence of Gaussian Noise

Mingjun Zhong, Huanwen Tang, Yiyuan Tang, 2004 .

Efficient Gradient-Free Variational Inference using Policy Search

Mingjun Zhong, Gerhard Neumann, Oleg Arenz, 2018, ICML.

A variational method for learning sparse Bayesian regression

Mingjun Zhong, 2006, Neurocomputing.

Latent Bayesian melding for integrating individual and population models

Charles A. Sutton, Mingjun Zhong, Nigel H. Goddard, 2015, NIPS.

Neural Control Variates for Variance Reduction pdf

Zhanxing Zhu, Mingjun Zhong, Ruosi Wan, 2018, ArXiv.

Letters Expectation-Maximization approaches to independent component analysis

Mingjun Zhong, Yiyuan Tang, Huanwen Tang, 2004 .

Reversible Jump MCMC for Non-Negative Matrix Factorization

Mingjun Zhong, Mark A. Girolami, 2009, AISTATS.

Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network

Ting Li, Huaming Chen, Mingjun Zhong, 2016 .

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09)

Mark Girolami, Mingjun Zhong, M. Girolami, 2009 .

A parametric density model for blind source separation

Mingjun Zhong, Junfu Du, Mingjun Zhong, 2007, Neural Processing Letters.

Expectation-Maximization approaches to independent component analysis

Mingjun Zhong, Huanwen Tang, Yiyuan Tang, 2004, Neurocomputing.

A comparative evaluation of stochastic-based inference methods for Gaussian process models

Maurizio Filippone, Mingjun Zhong, Mark A. Girolami, 2013, Machine Learning.

An EM algorithm for learning sparse and overcomplete representations

Hongjun Chen, Mingjun Zhong, Huanwen Tang, 2004, Neurocomputing.

Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network

Hong Tang, Ting Li, Huaming Chen, 2016, 2016 Computing in Cardiology Conference (CinC).

A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices pdf

Mingjun Zhong, Mark A. Girolami, M. Girolami, 2012, ICML.

Advances in Neural Information Processing Systems 19 (NIPS 2006)

Mark Girolami, Mingjun Zhong, M. Girolami, 2007 .

A Hyperplane Clustering Algorithm for Estimating the Mixing Matrix in Sparse Component Analysis

Chonghui Guo, Xu Xu, Mingjun Zhong, 2017, Neural Processing Letters.

Data Integration for Classification Problems Employing Gaussian Process Priors

Mingjun Zhong, Mark A. Girolami, 2006, NIPS.

Neural Networks and Brain, 2005. ICNN B '05. International Conference on

Mingjun Zhong, 2005 .

Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation

Charles A. Sutton, Mingjun Zhong, Nigel H. Goddard, 2014, NIPS.

Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data pdf

Bo Liu, Mingjun Zhong, Rong Liu, 2012, ArXiv.

Neural Control Variates for Monte Carlo Variance Reduction

Zhanxing Zhu, Haoyi Xiong, Mingjun Zhong, 2019, ECML/PKDD.

Classifying EEG for brain computer interfaces using Gaussian processes

Anatole Lécuyer, Mingjun Zhong, Fabien Lotte, 2008, Pattern Recognit. Lett..

Reliability analysis based on dynamic Bayesian networks: A case study of an unmanned surface vessel

Hongdong Wang, Mingjun Zhong, Hong Yi, 2021, Ocean Engineering.

Causal Effect Estimation using Variational Information Bottleneck pdf

Ye Yuan, Mingjun Zhong, Guoren Wang, 2021, ArXiv.

A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures

Hongdong Wang, Mingjun Zhong, Hong Yi, 2021, Reliab. Eng. Syst. Saf..

Bayesian methods to detect dye‐labelled DNA oligonucleotides in multiplexed Raman spectra

Mark Girolami, Mingjun Zhong, Duncan Graham, 2011 .