Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units—a different pattern of dropout is applied to every sample in the minibatch. We explore a very simple alternative to the dropout mask. Instead of masking dropped out units by setting them to zero, we per...

Address correspondence to: José M. Calderón, MS, Associate investigator, institute of Research, education and Services in Addiction, universidad Central del Caribe, School of Medicine, P.o. box 60-327, bayamón, Puerto Rico 00960-6032. Tel: 787-288-0200 • Fax: 787-288-0242 • E-mail: [email protected] Objective: This research aims to understand the circumstances associated with school dropou...

Student dropout prediction is an indispensable for numerous intelligent systems to measure the education system and success rate of any university as well as throughout the university in the world. Therefore, it becomes essential to develop efficient methods for prediction of the students at risk of dropping out, enabling the adoption of proactive process to minimize the situation. Thus, this r...

Single cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical dimensionality reduction methods because of the prev...

STUDY QUESTION What are the dropout rates in lifestyle intervention programs (LIPs) for overweight and obese infertile women and can intervention- or patient-related baseline factors associated with dropout be identified in these women? SUMMARY ANSWER The median dropout rate was 24% in overweight and obese infertile women who participated in a LIP; clinical useful intervention or patient-rela...

Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. is currently not grounded in explicit modelling assumptions which so far precluded its adoption Bayesian modelling. Using entropic reasoning we show that dropout can be interpreted optimal inference under constraints. We demonstrate this on an an...

Education is the backbone of any country and it is very important to improve the educational strength of the country. There are various methods and challenges on the way, use of technologies like computers, smart rooms, projectors, and eBooks. But these resources are useful only when we know which student needs which type of resource or, in other words, if we are able to predict the results of ...

We propose a marginalized joint-modeling approach for marginal inference on the association between longitudinal responses and covariates when longitudinal measurements are subject to informative dropouts. The proposed model is motivated by the idea of linking longitudinal responses and dropout times by latent variables while focusing on marginal inferences. We develop a simple inference proced...

The cross-entropy loss commonly used in deep learning is closely related to the defining properties of optimal representations, but does not enforce some of the key properties. We show that this can be solved by adding a regularization term, which is in turn related to injecting multiplicative noise in the activations of a Deep Neural Network, a special case of which is the common practice of d...