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Student Dropout Prediction [PDF]
, 2020 Among the many open problems in the learning process, students dropout is one of the most complicated and negative ones, both for the student and the institutions, and being able to predict it could help to alleviate its social and economic costs. To address this problem we developed a tool that, by exploiting machine learning techniques, allows to ...
Del Bonifro F. +3 more
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International Journal of Adolescence and Youth, 2023 Self-directed learning as a key component of lifelong learning has become increasingly important in a rapidly changing society. SDL competencies support continuous personal development and coping with change.
Kerli Kõiv, Katrin Saks
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CoRR, 2016 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.
Suraj Srinivas, R. Venkatesh Babu
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CoRR, 2017 Recurrent neural networks (RNNs) are important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is known to be harder compared to feed-forward neural networks. A number of techniques have been proposed in literature to address this problem.
Konrad Zolna +3 more
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The dropout learning algorithm [PDF]
Artificial Intelligence, 2014 Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable ...
Pierre Baldi, Peter J. Sadowski
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Radiation treatment dropouts-Pitfalls and solutions: A retrospective observational study
Cancer Research, Statistics, and Treatment, 2023 Background: Interruptions in radiation treatment are known to have a deleterious effect on oncologic outcomes,specifically, an increase in tumor recurrence and decrease in cancer cure rate.
Kanhu Charan Patro +8 more
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, 2017 Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimates, a grid-search over the dropout probabilities is necessary - a prohibitive operation with large models, and an impossible one with RL.
Gal, Y, Hron, J, Kendall, A
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Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes random drop of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout.
Rohit Keshari +2 more
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Nordic Journal of Vocational Education and Training, 2020 In Sweden, many students start but do not graduate from upper secondary school despite preventive efforts. The reasons for students dropping out of school have been examined and opposed, but there is still more to be done.
Ulrika Gidlund
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2017 IEEE International Conference on Computer Vision (ICCV), 2017 Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization.
Pietro Morerio +4 more
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