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...

Orthotopic liver transplantation (OLT) can be a definitive treatment for patients with hepatocellular carcinoma (HCC). Prolonged waiting times for cadaveric livers, however, may lead to dropout from the waiting list or worsened post-OLT prognosis as a result of interval tumor progression. Percutaneous radiofrequency ablation (RFA) is widely used for local control of small unresectable HCC, but ...

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...

Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system v...

Dropout is a relatively new algorithm for training neural networks which relies on stochastically “dropping out” neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in bot...

• Gal and Gharamani (2015) reinterpreted dropout regularisation as approximate inference in BNNs •Dropout probabilities pl are variational parameters of the approximate posterior qθ(ω) = ∏ k qMk,pk(Wk), where Wk = Mk · diag (zk) and zkl iid ∼Bernoulli(1− pk) • Concrete distribution (Maddison et al., Jang et al.) relaxes Categorical distribution to obtain gradients wrt the probability vector – E...