OBJECTIVE To study the impact of mental disorders on failure in educational attainment in Mexico. METHODS Diagnoses and age of onset for each of 16 DSM-IV disorders were assessed through retrospective self-reports with the Composite International Diagnostic Instrument (CIDI) during fieldwork in 2001-2002. Survival analysis was used to examine associations between early onset DSM-IV/CIDI disor...

Massive open online courses (MOOCs) have received wide publicity and many institutions have invested considerable effort in developing, promoting and delivering such courses. However, there are still many unresolved questions relating to MOOCs and their effectiveness. One of the major recurring issues raised in both academic literature and the popular press is the consistently high dropout rate...

Deep learning has become the state-of-art tool in many applications, but the evaluation and training of such models is very time-consuming and expensive. Dropout has been used in order to make the computations sparse (by not involving all units), as well as to regularize the models. In typical dropout, nodes are dropped uniformly at random. Our goal is to use reinforcement learning in order to ...

Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers, huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some inputs and hidden units in the learning process with a probability, p, and the...

Dropout has been raised as an effective and simple trick [1] to combat overfitting in deep neural nets. The idea is to randomly mask out input and internal units during training. Despite its usefulness, there has been very little and scattered understanding on injecting noise to deep learning architectures’ internal units. In this paper, we study the effect of dropout on both input and hidden l...

Average change in list recall was evaluated as a function of missing data treatment (Study 1) and dropout status (Study 2) over ages 70 to 105 in Asset and Health Dynamics of the Oldest-Old data. In Study 1 the authors compared results of full-information maximum likelihood (FIML) and the multiple imputation (MI) missing-data treatments with and without independent predictors of missingness. Re...

A holonomic function is a differentiable or generalized function which satisfies a holonomic system of linear partial or ordinary differential equations with polynomial coefficients. The main purpose of this paper is to present algorithms for computing a holonomic system for the definite integral of a holonomic function with parameters over a domain defined by polynomial inequalities. If the in...

The recent advances of deep learning in applied machine learning gained tremendous success, addressing the problem of learning from massive amounts of data. However, the challenge now is to learn data-efficiently with the ability to learn in complex domains without requiring deep learning models to be trained with large quantities of data. We present the novel framework of achieving data-effici...

BACKGROUND To answer the question of what bio-psychosocial determinants in infancy, early and middle childhood, and adolescence predict school drop-out in young adulthood, we approached the complex process towards school dropout as a multidimensional, life-course phenomenon. The aim is to find signs of heightened risks of school dropout as early as possible which will eventually help public hea...