Zhiyuan Chaudhri's Books

Lifelong Machine Learning

By: Zhiyuan Chaudhri,Bing Liu

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Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.

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Page Count

137

Publisher

Springer Nature

Published Date

ISBN 10

3031015754

ISBN 13

9783031015755

Introduction to Graph Neural Networks

By: Zhiyuan Zhiyuan Liu,Jie Jie Zhou

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Introduction to Graph Neural Networks

By: Zhiyuan Zhiyuan Liu,Jie Jie Zhou

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

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Page Count

109

Publisher

Springer Nature

Published Date

ISBN 10

3031015878

ISBN 13

9783031015878

Network Embedding

Theories, Methods, and Applications

By: Cheng Cheng Yang,Zhiyuan Zhiyuan Liu,Cunchao Cunchao Tu,Chuan Chuan Shi,Maosong Maosong Sun

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Network Embedding

Theories, Methods, and Applications

By: Cheng Cheng Yang,Zhiyuan Zhiyuan Liu,Cunchao Cunchao Tu,Chuan Chuan Shi,Maosong Maosong Sun

heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.

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Page Count

220

Publisher

Springer Nature

Published Date

ISBN 10

3031015908

ISBN 13

9783031015908

Lifelong Machine Learning, Second Edition

By: Zhiyuan Sun,Bing Leno da Silva

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Lifelong Machine Learning, Second Edition

By: Zhiyuan Sun,Bing Leno da Silva

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

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Page Count

187

Publisher

Springer Nature

Published Date

ISBN 10

3031015819

ISBN 13

9783031015816

Book's by Zhiyuan Chaudhri

Lifelong Machine Learning

Lifelong Machine Learning

Introduction to Graph Neural Networks

Introduction to Graph Neural Networks

Network Embedding

Network Embedding

Lifelong Machine Learning, Second Edition

Lifelong Machine Learning, Second Edition

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