We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for task detecting out distribution (OOD) data. first show how previous attempts to leverage randomized embeddings induced by intermediate layers BNN can fail due distance metric used. introduce an alternative approach measuring embedding uncertainty, and demonstrate incorporating uncertainty im...

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...

Dropouts impact clinical trial outcome analyses. Ignoring missing data is not an acceptable option when planning, conducting or interpreting the analysis of a clinical trial. Treatment related efficacy and safety data observed in the trial may not always be sufficient in explaining the dropouts' mechanism. Nevertheless, these dropout data may carry important treatment-related information and pr...

Financial services industries, such as insurance, increasingly use data from their broad cross-section of customers and follow these over time. In other areas medicine, engineering, communication systems, it is well known that following subjects time may result in biased data, for example, the so-called ”dropout effect”. This paper introduces techniques to address dropout commonly encountered i...