A very commonly faced issue while training prediction models using machine learning is overfitting. Dropout is a recently developed technique designed to counter this issue in deep neural networks and has also been extended to other algorithms like SVMs. In this project, we formulate and study the application of Dropout to Hidden Unit Conditional Random Fields (HUCRFs). HUCRFs use binary stocha...

Previous reviews of premature termination have yet to examine whether disparate psychotherapy treatments differ in their dropout rates for specific disorders. Using data from 587 studies, a series of meta-analyses were conducted comparing dropout rates between treatment approaches for 12 separate disorder categories. Although, significant differences between treatment approaches were found for ...

INTRODUCTION The dropout level from the Danish medical schools is high, but we have only little insight into this problem. The purpose of this study was to qualify the ongoing discussions concerning dropout. MATERIAL AND METHODS In this retrospective cohort study, relevant variables were extracted from the established database of Aarhus University for the 639 students initiating medicine stud...

Many psychology applications assess measurement invariance of a construct (e.g., depression) over time. These applications are often characterized by few time points (e.g., 3), but high rates of dropout. Although such applications routinely assume that the dropout mechanism is ignorable, this assumption may not always be reasonable. In the presence of nonignorable dropout, fitting a conventiona...

We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations ...

Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach program to ascertain the vital status for dropout subjects. These data can be used to identify a number of r...

A large percentage of individuals who enter residential substance abuse treatment drop out before completing treatment. Given that early treatment dropout places individuals at an increased risk for relapse, identifying the mechanisms underlying treatment dropout would have several important theoretical and clinical implications. In the current study, the authors examined levels of psychologica...

School dropout and child marriage are interrelated outcomes that have an enormous impact on adolescent girls. However, the literature reveals gaps in the empirical evidence on the link between child marriage and the dropout of girls from school. This study identifies the 'tipping point' school grades in Nepal when the risk of dropout due to marriage is highest, measures the effect of child marr...

Deep learning is a trending topic widely studied by researchers due to increase in the abundance of data and getting meaningful results with them. Convolutional Neural Networks (CNN) is one of the most popular architectures used in deep learning. Binarized Neural Network (BNN) is also a neural network which consists of binary weights and activations. Neural Networks has large number of paramete...

OBJECTIVE To determine the association between use of school-based health centers (SBHCs) and school dropout. DESIGN Quasi-experimental longitudinal analysis of a retrospective student cohort, with SBHC use as the independent variable. We statistically controlled for dropout risk and used propensity score regression adjustment to control for several factors associated with SBHC use. SETTING...