Ying Gu Ben Jiang Shan Yong Can Gao
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Ying Gu Ben Jiang Shan Yong Can Gao
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Author : Yanwu Gu
language : zh-CN
Publisher:
Release Date : 1963
Ying Gu Ben Jiang Shan Yong Can Gao written by Yanwu Gu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1963 with Scholars categories.
Gu Ben Jiu Chao Jiang Shanyong Can Gao
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Author : Yanwu Gu
language : zh-CN
Publisher:
Release Date : 1963
Gu Ben Jiu Chao Jiang Shanyong Can Gao written by Yanwu Gu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1963 with categories.
Water Margin
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Author : Nai'an Shi
language : en
Publisher:
Release Date : 1976
Water Margin written by Nai'an Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976 with categories.
Federated Learning
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Author : Qiang Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Federated Learning written by Qiang Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Graph Representation Learning
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Author : William L. William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
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Author :
language : en
Publisher:
Release Date :
written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Social Evolution
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Author : Benjamin Kidd
language : en
Publisher:
Release Date : 1898
Social Evolution written by Benjamin Kidd and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1898 with Sociology categories.
Local Traditional And Indigenous Food Systems In The 21st Century To Combat Obesity Undernutrition And Climate Change 2nd Edition
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Author : Rebecca Kanter
language : en
Publisher: Frontiers Media SA
Release Date : 2023-07-31
Local Traditional And Indigenous Food Systems In The 21st Century To Combat Obesity Undernutrition And Climate Change 2nd Edition written by Rebecca Kanter and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Medical categories.
Traditional and indigenous food systems have existed for centuries and were in balance with local food supplies, globally. However, between the mid 20th and early 21st century the green revolution dramatically altered food production, which in turn affected the inclusivity of traditional production systems within food systems and subsequently, traditional dietary intakes. This change was accompanied by lifestyle changes and spurred a global nutrition transition. Today the world faces a global syndemic of obesity, undernutrition, and climate change. A new call to action to create food systems that nourish people and sustain the planet is needed. Traditional and indigenous food systems have long been recognized as systems that can both support good human nutrition as well as maintain a balance with nature. There is an underutilized knowledge base around traditional and indigenous food systems. This includes the knowledge of nutritious species, traditional culinary preparations, and cultural practices. Greater agricultural production of underutilized species can result in more sustainable agricultural and food systems which can also help improve livelihoods and food security. Traditional and indigenous cultural practices with respect to both land and water management, as well as culinary practices, contribute to both sustainable food production and consumption. These practices require a greater evidence base in order to be incorporated into public health nutrition initiatives related to improving dietary quality, such as food-based dietary guidelines for example. An increased focus on the importance of local, traditional, and indigenous food systems and nutrition could therefore help countries to improve human nutrition and, ideally, help mitigate the global syndemic of obesity, undernutrition, and climate change. This Research Topic will focus on documenting diverse local food systems and promoting elements within them that can help improve nutrition and health – both human and planetary - in various ways including the livelihood development of knowledge holders.
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Author : Samuel Wells Williams
language : en
Publisher:
Release Date : 1874
written by Samuel Wells Williams and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1874 with Chinese language categories.
Advances In Neural Networks Isnn 2007
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Author : Derong Liu
language : en
Publisher: Springer
Release Date : 2007-07-14
Advances In Neural Networks Isnn 2007 written by Derong Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-14 with Computers categories.
This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.