While the subject of child dropout has been heavily contested in education studies in the last few decades in developing countries like Zimbabwe, the monumental studies on the concept have emphasised child’s (both boy and girl child) right to education. Insignificant attention has been devoted to examining the causes and effects of girl child dropouts in secondary schools especially in the coun...

Dropout is typically interpreted as bagging a large number of models sharing parameters. We show that using dropout in a network can also be interpreted as a kind of data augmentation in the input space without domain knowledge. We present an approach to projecting the dropout noise within a network back into the input space, thereby generating augmented versions of the training data, and we sh...

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...

Title of Thesis: SCHOOL DROPOUT AND SUBSEQUENT OFFENDING: DISTINGUISHING SELECTION FROM CAUSATION Gary Allen Sweeten, Master of Arts, 2004 Thesis directed by: Professor Shawn D. Bushway Department of Criminology and Criminal Justice Past research on the relationship between school dropout and offending is inconclusive. In explaining their findings, researchers have focused on strain and control...

Color dropout refers to the process of converting color form documents to black and white by removing the colors that are part of the blank form and maintaining only the information entered in the form. In this paper, no prior knowledge of the form type is assumed. Color dropout is performed by associating darker non-dropout colors with information that is entered in the form and needs to be pr...

Regularizing neural networks is an important task to reduce overfitting. Dropout [1] has been a widely-used regularization trick for neural networks. In convolutional neural networks (CNNs), dropout is usually applied to the fully connected layers. Meanwhile, the regularization effect of dropout in the convolutional layers has not been thoroughly analyzed in the literature. In this paper, we an...

We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse sol...