Results 31 to 40 of about 6,572,773 (295)
Longitudinal self-supervised learning [PDF]
Medical Image Analysis, 2021 Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to ...
Qingyu Zhao +3 more
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Cross-supervised learning for cloud detection
GIScience & Remote Sensing, 2023 We present a new learning paradigm, that is, cross-supervised learning, and explore its use for cloud detection. The cross-supervised learning paradigm is characterized by both supervised training and mutually supervised training, and is performed by two
Kang Wu +3 more
doaj +1 more source
Remote Sensing, 2022 In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted
Xiliang Chen, Guobin Zhu, Mingqing Liu
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CoRR, 2017 Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models.
Yingce Xia +5 more
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Neural Flocking: MPC-based Supervised Learning of Flocking Controllers
, 2020 We show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data,
Mehmood, Usama +5 more
core +4 more sources
Augmenting Few-Shot Learning With Supervised Contrastive Learning
IEEE Access, 2021 Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios.
Taemin Lee, Sungjoo Yoo
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Supervised Machine Learning a Brief Survey of Approaches
Al-Iraqia Journal for Scientific Engineering Research, 2023 Machine learning has become popular across several disciplines right now. It enables machines to automatically learn from data and make predictions without the need for explicit programming or human intervention. Supervised machine learning is a popular
Esraa Najjar, Aqeel Majeed Breesam
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Supervising Unsupervised Learning
CoRR, 2017 We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms.
Vikas K. Garg 0001, Adam Kalai
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Supervised learning and Co-training [PDF]
Theoretical Computer Science, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Malte Darnstädt +2 more
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Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context [PDF]
, 2022 Background and objectives: The development of mobile and on the edge appli-cations that embed deep convolutional neural models has the potential to revolutionisebiomedicine.
Díaz-Pinto, Andrés +5 more
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