Faming Liang

According to our database1, Faming Liang authored at least 40 papers between 1999 and 2020.

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Bibliography

2020
Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence.
IEEE Trans. Medical Imaging, 2020

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols.
CoRR, 2020

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction.
CoRR, 2020

Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts.
CoRR, 2020

Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection.
CoRR, 2020

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model.
IEEE J. Biomed. Health Informatics, 2019

Double-Parallel Monte Carlo for Bayesian analysis of big data.
Stat. Comput., 2019

Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data.
Neural Comput., 2019

An Adaptive Empirical Bayesian Method for Sparse Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017
Parallel and interacting stochastic approximation annealing algorithms for global optimisation.
Stat. Comput., 2017

A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data.
J. Comput. Biol., 2017

Enhanced construction of gene regulatory networks using hub gene information.
BMC Bioinform., 2017

2016
A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.
Technometrics, 2016

Comment: "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization" by Gramacy et al.
Technometrics, 2016

2014
Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities.
Stat. Comput., 2014

Bayesian Peak Picking for NMR Spectra.
Genom. Proteom. Bioinform., 2014

Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants.
Comput. Stat. Data Anal., 2014

2013
A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants.
Neural Comput., 2013

Statistical Properties of Horizontally Oriented Plates in Optically Thick Clouds From Satellite Observations.
IEEE Geosci. Remote. Sens. Lett., 2013

2011
Annealing evolutionary stochastic approximation Monte Carlo for global optimization.
Stat. Comput., 2011

Folding small proteins via annealing stochastic approximation Monte Carlo.
Biosyst., 2011

2010
Modeling the Relationship Between EDI Implementation and Firm Performance Improvement With Neural Networks.
IEEE Trans Autom. Sci. Eng., 2010

A hidden Ising model for ChIP-chip data analysis.
Bioinform., 2010

2009
Stochastic Approximation Monte Carlo for MLP Learning.
Proceedings of the Encyclopedia of Artificial Intelligence (3 Volumes), 2009

Learning Bayesian networks for discrete data.
Comput. Stat. Data Anal., 2009

Bayesian modeling of ChIP-chip data using latent variables.
BMC Bioinform., 2009

2008
Adaptive evolutionary Monte Carlo algorithm for optimization with applications to sensor placement problems.
Stat. Comput., 2008

Phylogenetic tree construction using sequential stochastic approximation Monte Carlo.
Biosyst., 2008

2007
Dynamic agglomerative clustering of gene expression profiles.
Pattern Recognit. Lett., 2007

Annealing stochastic approximation Monte Carlo algorithm for neural network training.
Mach. Learn., 2007

Use of SVD-based probit transformation in clustering gene expression profiles.
Comput. Stat. Data Anal., 2007

2006
Statistical and Computational Inverse Problems.
Technometrics, 2006

2005
Bayesian neural networks for nonlinear time series forecasting.
Stat. Comput., 2005

Evidence Evaluation for Bayesian Neural Networks Using Contour Monte Carlo.
Neural Comput., 2005

Efficient MCMC estimation of discrete distributions.
Comput. Stat. Data Anal., 2005

2003
An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine.
Neural Comput., 2003

2000
Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application.
Proceedings of ASP-DAC 2000, 2000

1999
Relaxed Simulated Tempering for VLSI Floorplan Designs.
Proceedings of the 1999 Conference on Asia South Pacific Design Automation, 1999


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