Dropout is a method that prevents overfitting when training deep neural networks. It involves sampling different sub-networks by temporarily removing nodes at random. Dropout works well in practice, but its properties have not been fully explored or theoretically justified. Our project explores the properties of dropout by applying methods used in optimization such as simulated annealing and lo...

OBJECTIVE To examine the disparities in psychiatric service utilisation over a 10-year period for patients with first admission for psychosis in relation to urban-rural residence following the implementation of universal health coverage in Taiwan. DESIGN Population-based retrospective cohort study. SETTING Taiwan's National Health Insurance Research Database, which has a population coverage...

Dropout is a common problem in the treatment of psychiatric illnesses including bipolar disorders (BD). The aim of the present study is to investigate illness perceptions of dropout patients with BD. A cross sectional study was done on the participants who attended the Mood Disorder Outpatient Clinic at least 3 times from January 2003 through June 2008, and then failed to attend clinic till to ...

The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient rand...

We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserve the expected input to a neuron between train and test, the variance of the input differs. We thus propose a new weight initialization by correcting for the influence of dropout rates and an arbitrary nonlinearity’s ...

OBJECTIVE The aim of this study was to use pretreatment and treatment factors to predict dropout from residential substance use disorder program and to examine how the treatment environment modifies the risk for dropout. METHOD This study assessed 3649 male patients at entry to residential substance use disorder treatment and obtained information about their perceptions of the treatment envir...