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Exploring Simple Siamese Representation Learning [PDF]
Computer Vision and Pattern Recognition, 2020 Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing ...
Xinlei Chen, Kaiming He
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An introduction to statistical learning with applications in R
Statistical Theory and Related Fields, 2021 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Fariha Sohil +2 more
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Momentum Contrast for Unsupervised Visual Representation Learning [PDF]
Computer Vision and Pattern Recognition, 2019 We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large
Kaiming He +4 more
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Machine Learning: Algorithms, Real-World Applications and Research Directions
SN Computer Science, 2021 In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc.
Iqbal H. Sarker
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Acknowledgment to Reviewers of Machine Learning and Knowledge Extraction in 2021
Machine Learning and Knowledge Extraction, 2022 Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Machine Learning and Knowledge Extraction Editorial Office
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Acknowledgment to the Reviewers of Machine Learning and Knowledge Extraction in 2022
Machine Learning and Knowledge Extraction, 2023 High-quality academic publishing is built on rigorous peer review [...]
Machine Learning and Knowledge Extraction Editorial Office
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Advances and Open Problems in Federated Learning [PDF]
Found. Trends Mach. Learn., 2019 Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g.
P. Kairouz +57 more
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Research and Practice in Technology Enhanced Learning, 2021 An amendment to this paper has been published and can be accessed via the original article.
Research and Practice in Technology Enhanced Learning
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A Survey on Bias and Fairness in Machine Learning [PDF]
ACM Computing Surveys, 2019 With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems.
Ninareh Mehrabi +4 more
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Learning sites for health system governance in Kenya and South Africa: reflecting on our experience
Health Research Policy and Systems, 2020 Background Health system governance is widely recognised as critical to well-performing health systems in low- and middle-income countries. However, in 2008, the Alliance for Health Policy and Systems Research identified governance as a neglected health ...
The RESYST/DIAHLS learning site team
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