In the field of the architectural heritage, the representation of artefacts, particularly for communication purposes, has benefited from the development of computer-based modelling techniques in fields ranging form archaeology to geography. But numerous experts in the above mentioned heritage field have come to question the readability of realistic models inside which the hypothetical nature of...

Missing values occur in studies of various disciplines such as social sciences, medicine, and economics. The missing mechanism in these studies should be investigated more carefully. In this article, some models, proposed in the literature on longitudinal data with dropout are reviewed and compared. In an applied example it is shown that the selection model of Hausman and Wise (1979, Econometri...

Dropout in randomised controlled trials is common and threatens the validity of results, as completers may differ from people who drop out. Differing dropout rates between treatment arms is sometimes called differential dropout or attrition. Although differential dropout can bias results, it does not always do so. Similarly, equal dropout may or may not lead to biased results. Depending on the ...

We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that ...

Recurrent Neural Networks are very powerful computational tools that are capable of learning many tasks across different domains. However, it is prone to overfitting and can be very difficult to regularize. Inspired by Recurrent Dropout [1] and Skip-connections [2], we describe a new and simple regularization scheme: Stochastic Dropout. It resembles the structure of recurrent dropout, but offer...

Deep Neural Networks often require good regularizers to generalize well. Dropout is one such regularizer that is widely used among Deep Learning practitioners. Recent work has shown that Dropout can also be viewed as performing Approximate Bayesian Inference over the network parameters. In this work, we generalize this notion and introduce a rich family of regularizers which we call Generalized...

Dropout of sport is an issue in sport and public health domains. The aim of this study was to identify the potential dropout reasons of school athletes and to examine if their perception of dropout was affected by the previous dropout experience. There were 50 subjects who were divided into two groups based on their previous dropout experience (Dropout Group=22, No Dropout Group=28). They fille...

Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager et al. We focus on linear classification where a convex proxy to the misclass...

Do Tertiary Dropout Students Really Not Succeed in European Labour Markets? Tertiary education has been expanding hugely over the last decades, so that tertiary dropout students will constitute a growing distinctive group in future labour markets. University dropout is regularly discussed as a ‘negative’ indicator in terms of reinforcing socio-economic inequalities and being a sign of universit...