2 citations · 3 across the 5 of their papers we have counts for
6 papers
Poisson Hyperplane Processes with Rectified Linear Units
Shufei Ge, Shijia Wang, Lloyd Elliott
Neural networks have shown state-of-the-art performances in various classification and regression tasks. Rectified linear units (ReLU) are often used as activation functions for th…
NExt-LMM: a two-stage LMM framework with near-exact variance components for genome-wide association studies
Zhibin Pu, Shufei Ge, Shijia Wang
Linear mixed models (LMMs) are widely used in genome-wide association studies (GWAS) to account for population stratification and cryptic relatedness. However, their practical appl…
Shape Modeling with Spline Partitions
Shufei Ge, Shijia Wang, Lloyd Elliott
Shape modelling (with methods that output shapes) is a new and important task in Bayesian nonparametrics and bioinformatics. In this work, we focus on Bayesian nonparametric method…
Adaptive semiparametric Bayesian differential equations via sequential Monte Carlo
Shijia Wang, Shufei Ge, Renny Doig +1
Nonlinear differential equations (DEs) are used in a wide range of scientific problems to model complex dynamic systems. The differential equations often contain unknown parameters…
Random Tessellation Forests
Shufei Ge, Shijia Wang, Yee Whye Teh +2
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursi…
A Bayesian Spatial Model for Imaging Genetics
Yin Song, Shufei Ge, Jiguo Cao +2
We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model…