Search Results for author:
Found 7 papers, 0 papers with code
Deep Learning for Efficient GWAS Feature Selection
Our extended approach enhances the original method by introducing a Frobenius norm penalty into the student network, augmenting its capacity to adapt to scenarios characterized by a multitude of features and limited samples.
On the Confidence Intervals in Bioequivalence Studies
A bioequivalence study is a type of clinical trial designed to compare the biological equivalence of two different formulations of a drug.
Semiparametric Regression for Spatial Data via Deep Learning
To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence.
Variable selection for nonlinear Cox regression model via deep learning
Variable selection problem for the nonlinear Cox regression model is considered.
Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks
The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption.
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure.
Nonparametric Inference under B-bits Quantization
Statistical inference based on lossy or incomplete samples is often needed in research areas such as signal/image processing, medical image storage, remote sensing, signal transmission.
