Regularization is important for end-to-end speech models, since the models are highly flexible and easy to overfit. Data augmentation and dropout has been important for improving end-to-end models in other domains. However, they are relatively under explored for end-to-end speech models. Therefore, we investigate the effectiveness of both methods for end-to-end trainable, deep speech recognitio...

Abstratct. This work focuses on data mining in relational databases, aiming to detect behaviors related to school dropouts and disapproval by mapping the factors that influence this dropout. This work is relevant by the fact that the dropout and school disapproval are big factors of concern to all who care about education in Brasil. At the end of it, we intend to point out the need to implement...

The widely used distinction of Little and Rubin (1) about types of randomness for missing data presents difficulties in its application to dropouts in longitudinal repeated measurement studies. In its place, a new typology of randomness for dropouts is proposed that relies on using a survival model for the dropout process. In terms of a stochastic process, dropping out is a change of state. The...

Survival studies usually collect on each participant, both duration until some terminal event and repeated measures of a time-dependent covariate. Such a covariate is referred to as an internal time-dependent covariate. Usually, some subjects drop out of the study before occurrence of the terminal event of interest. One may then wish to evaluate the relationship between time to dropout and the ...

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...

School absenteeism (or truancy) may be a signal of an ongoing process of student attrition that eventually leads to early school leaving. This paper estimates by a duration model the influence of school absenteeism on the risk of dropout in secondary education. In particular, using a semiparametric Cox duration model, the timing of the dropout decision of truants is compared with the timing of ...

Randomised controlled trials (RCTs) in arthritis and musculoskeletal conditions generally necessitate longterm follow up of largely self-reported outcomes; thus, such RCTs are prone to missing outcome data, mainly because of participant dropout/non-response. Recent years have seen a rise in the application of methods for dealing with missing outcome data (e.g. mixed models for repeated measures...

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...

We use all available fields with deep NICMOS imaging to search for J 110-dropouts (H 160,AB 28) at z ≈ 10. Our primary data set for this search were the two J 110 + H 160 NICMOS parallel fields taken with the Advanced Camera for Surveys (ACS) Hubble Ultra Deep Field (HUDF). The 5σ limiting magnitudes were 28.6 in J 110 and 28.5 in H 160 (0.6 ′′ apertures). Several shallower fields were also use...

SUMMARY Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process a...