Results 41 to 50 of about 136,861 (303)
Unsupervised learning in noise [PDF]
IEEE Transactions on Neural Networks, 1990 A new hybrid learning law, the differential competitive law, which uses the neuronal signal velocity as a local unsupervised reinforcement mechanism, is introduced, and its coding and stability behavior in feedforward and feedback networks is examined.
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Towards Open Ended Learning: Budgets, Model Selection, and Representation [PDF]
, 2011 Biological organisms learn to recognize visual categories continuously over the course of their lifetimes. This impressive capability allows them to adapt to new circumstances as they arise, and to flexibly incorporate new object categories as they are ...
Gomes, Ryan Geoffrey
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SUN: Stochastic UNsupervised Learning for Data Noise and Uncertainty Reduction
Applied SciencesUnsupervised learning methods significantly benefit various practical applications by effectively identifying intrinsic patterns within unlabelled data.
Nicholas Christakis, Dimitris Drikakis
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A comment on the training of unsupervised neural networks for learning phases
Results in Physics, 2022 The impact on the performance of an unsupervised neural network (NN) for learning the phases of two-dimensional ferromagnetic Potts model, namely a deep learning autoencoder (AE), from using various training sets is investigated.
Yuan-Heng Tseng, Fu-Jiun Jiang
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Representational Bias in Unsupervised Learning of Syllable Structure
, 2005 Unsupervised learning algorithms based on Expectation Maximization (EM) are often straightforward to implement and provably converge on a local likelihood maximum. However, these algorithms often do not perform well in practice.
Johnson, Mark +3 more
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Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis
Remote Sensing, 2022 The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities and challenges for land cover analysis. Many supervised machine learning methods have been applied in SITS, but the labeled SITS samples are time- and effort ...
Wenqi Guo +4 more
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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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UniverseThis review investigates the application of unsupervised machine learning algorithms to astronomical data. Unsupervised machine learning enables researchers to analyze large, high-dimensional, and unlabeled datasets and is sometimes considered more ...
Chih-Ting Kuo, Duo Xu, Rachel Friesen
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Sensors, 2022 This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models.
Baigan Zhao +3 more
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Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
Applied SciencesThis paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning.
Chunchun Wang, Shu Lv
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