Results 21 to 30 of about 215,422 (309)
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
Entropy, 2021 Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing.
Rami Ahmed +7 more
doaj +1 more source
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
openaire +3 more sources
Preventing School Dropout With Secondary Students: The Implementation of an Individualized Reading and Dropout Prevention Intervention [PDF]
, 2011 Society for Research on Educational Effectiveness (March 2011) Jade Wexler, Sharon Vaughn, Greg Roberts, Nicole Pyle, Anna-Mária Fall: Preventing School Dropout With Secondary Students: The Implementation of an Individualized Reading and Dropout ...
Wexler, J. +4 more
core +2 more sources
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
openaire +2 more sources
Dropout-based Adversarial Training Networks for Remote Sensing Scene Classification [PDF]
, 2022 Scene classification in remote sensing (RS) images is a challenging task due to the lack of well labeled data. Recently, deep transfer learning (DTL) has been proposed to handle this task.
X Wang (1378959) +4 more
core +2 more sources
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
openaire +3 more sources
, 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
openaire +4 more sources
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
openaire +3 more sources
Preventing School Dropout With Secondary Students [PDF]
, 2011 Council for Exceptional Children (April 2011) Jade Wexler, Sharon Vaughn, Greg Roberts, Nicole Pyle, Anna-Mária Fall, Jacob Williams, Leah Sayre: Preventing School Dropout With Secondary ...
Wexler, J. +8 more
core +1 more source
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
openaire +2 more sources