Results 21 to 30 of about 512,844 (338)

Electron dropout echoes induced by interplanetary shock: Van Allen Probes observations

Geophysical Research Letters, 2016
On 23 November 2012, a sudden dropout of the relativistic electron flux was observed after an interplanetary shock arrival. The dropout peaks at ∼1 MeV and more than 80% of the electrons disappeared from the drift shell.
Y. X. Hao, Q.‐G. Zong, X.‐Z. Zhou, S. Y. Fu, R. Rankin, C.‐J. Yuan, A. T. Y. Lui, H. E. Spence, J. B. Blake, D. N. Baker, G. D. Reeves   +10 more
doaj   +1 more source

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, Mandar Gogate, Ahsen Tahir, Kia Dashtipour, Bassam Al-tamimi, Ahmad Hawalah, Mohammed A. El-Affendi, Amir Hussain   +7 more
doaj   +1 more source

Guided Dropout

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, Richa Singh 0001, Mayank Vatsa   +2 more
openaire   +3 more sources

Generalized Dropout

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

On the cause and extent of outer radiation belt losses during the 30 September 2012 dropout event [PDF]

, 2014
On 30 September 2012, a flux dropout occurred throughout Earth\u27s outer electron radiation belt during the main phase of a strong geomagnetic storm.
Angelopoulos, V., Baker, D. N., Blake, J. B., Boyd, A. J., Claudepierre, S., Henderson, M. G., Huang, Chia-Lin L., Li, W., Morley, S. K., Reeves, Geoffrey, Rodriguez, J. V., Spence, Harlan E., Turner, D. L.   +12 more
core   +3 more sources

Concrete Dropout

, 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

Fraternal Dropout

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, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio   +3 more
openaire   +3 more sources

Methods for Evaluating Respondent Attrition in Web-Based Surveys [PDF]

, 2016
Background: Electronic surveys are convenient, cost effective, and increasingly popular tools for collecting information. While the online platform allows researchers to recruit and enroll more participants, there is an increased risk of participant ...
Cyrus, John, Day, Teresa, Hochheimer, Camille J, Krist, Alex H, Sabo, Roy T, Woolf, Steven H   +5 more
core   +3 more sources

DRN-LSTM: A Deep Residual Network Based On Long Short-term Memory Network For Students Behaviour Recognition In Education

Journal of Applied Science and Engineering, 2022
In classroom teaching, artificial intelligence technology can help automate student behavior analysis and enable teachers to master learning efficiently and intuitively provide data support for subsequent optimization of teaching design and ...
Zhaozhen Xuan
doaj   +1 more source

Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

, 2018
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels.
Chin, Peter, Kaeli, David, Lin, Xue, Wang, Siyue, Wang, Xiao, Wen, Wujie, Zhao, Pu   +6 more
core   +1 more source