Results 31 to 40 of about 8,207,067 (292)
e-Learning at the Coimbra Group Universities
Je-LKS: Journal of E-Learning and Knowledge Society, 2012 This document is the result of a Strategic Workshop held in Leuven by the Task Force e-Learning (TF eL) of the Coimbra Group (CG), followed by a series of meetings with the TF members on this issue.
Task Force e-Learning Coimbra Group
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A Comprehensive Survey on Transfer Learning [PDF]
Proceedings of the IEEE, 2019 Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains.
Fuzhen Zhuang +7 more
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MedEdPORTAL, 2015 Introduction Bloom's Taxonomy has become a gold standard for writing learning objectives. Originally developed in the 1950's by Benjamin Bloom and revised in the 1990's by Lorin Anderson, Bloom's Taxonomy is a hierarchical model for organizing thinking ...
Jeanne Schlesinger +2 more
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node2vec: Scalable Feature Learning for Networks [PDF]
Knowledge Discovery and Data Mining, 2016 Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by ...
Aditya Grover, J. Leskovec
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ONLINE LEARNING – BETWEEN NECESSITY AND OPTION
Journal of Education, Society & Multiculturalism, 2021 The analysis of the educational policy in relation to online education takes into account major decisions that have a direct impact on all those involved in taking these decisions and, of course, on how they become functional.
ONLINE LEARNING – BETWEEN NECESSITY AND OPTION
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Deep Learning with Differential Privacy [PDF]
Conference on Computer and Communications Security, 2016 Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information.
Martín Abadi +6 more
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Multimodal Machine Learning: A Survey and Taxonomy [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it ...
T. Baltrušaitis +2 more
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [PDF]
IEEE Transactions on Image Processing, 2016 The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
K. Zhang +4 more
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A survey on Image Data Augmentation for Deep Learning
Journal of Big Data, 2019 Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very
Connor Shorten, T. Khoshgoftaar
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Learning machine learning [PDF]
Communications of the ACM, 2018 A discussion of the rapidly evolving realm of machine learning.
Ted G. Lewis, Peter J. Denning
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