Results 11 to 20 of about 8,207,067 (292)
Learning to Prompt for Vision-Language Models [PDF]
International Journal of Computer Vision, 2021 Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks.
Kaiyang Zhou +3 more
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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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Technometrics, 2021 A machine learning system, in general, learns from the environment, but statistical machine learning programs (systems) learn from the data. This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to ...
Yuhai Wu
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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
semanticscholar +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
semanticscholar +1 more source
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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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
semanticscholar +1 more source