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Titlebook: Unsupervised Learning in Space and Time; A Modern Approach fo Marius Leordeanu Book 2020 Springer Nature Switzerland AG 2020 Computer Visio

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發(fā)表于 2025-3-21 17:09:50 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Unsupervised Learning in Space and Time
副標題A Modern Approach fo
編輯Marius Leordeanu
視頻videohttp://file.papertrans.cn/943/942527/942527.mp4
概述Offers a novel approach to unsupervised learning, which connects seemingly disparate problems in the domain through unified mathematical formulations and efficient optimization algorithms.Explains, in
叢書名稱Advances in Computer Vision and Pattern Recognition
圖書封面Titlebook: Unsupervised Learning in Space and Time; A Modern Approach fo Marius Leordeanu Book 2020 Springer Nature Switzerland AG 2020 Computer Visio
描述.This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field...Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts...Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems ar
出版日期Book 2020
關(guān)鍵詞Computer Vision; Deep Learning; Unsupervised Learning; Applications of Convolutional Neural Networks; Gr
版次1
doihttps://doi.org/10.1007/978-3-030-42128-1
isbn_softcover978-3-030-42130-4
isbn_ebook978-3-030-42128-1Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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發(fā)表于 2025-3-21 20:34:19 | 只看該作者
Marius Leordeanulop methods for figuring out how much an app can detect and collect from its users, and whether that access is in line with their expectations of privacy. Several methods have been devised to determine app intrusiveness, including analysis of their descriptions and conformity with their programmed b
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發(fā)表于 2025-3-22 08:23:27 | 只看該作者
Marius LeordeanuAutism Spectrum Disorder (ASD) therapy. This robot has speakers and color (RGB) and infrared cameras, allowing it to emit artificial voice (to motivate the children) and capture RGB and thermal images from the children to infer their emotions. The robot can operate autonomously (using its onboard se
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發(fā)表于 2025-3-22 08:52:06 | 只看該作者
Marius Leordeanueen in dealing with numbers and verbal communications. The automatic recognition of facial expressions is of theoretical and commercial interests and to this end there must exist video databases that incorporate the idiosyncrasies of human existence – ethnicity, gender and age. We compare the perfor
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發(fā)表于 2025-3-22 20:51:59 | 只看該作者
Marius Leordeanu can utilize and pay for only needed resources, thereby avoiding large upfront investment. Some also believe that in addition to its promise of demand elasticity, the cloud also offers security of data. We analyze this security aspect in this paper. Protection of data stored in the cloud is intertwi
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發(fā)表于 2025-3-23 00:08:25 | 只看該作者
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發(fā)表于 2025-3-23 04:02:54 | 只看該作者
Marius Leordeanul goods, industrial automation, and energy, have adopted the Internet of Things. It has been used more than 14 billion networked devices globally, or almost two devices per person. The internet of things links inanimate items and allows them to engage with their network to systematise human activiti
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發(fā)表于 2025-3-23 09:30:48 | 只看該作者
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