Search results for "tag:"Author Jian Pei""
Springer Verlag, Singapore Graph Neural Networks: Foundations, Frontiers, and Applications
Book SynopsisDeep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.Table of ContentsChapter 1. Representation Learning.- Chapter 2. Graph Representation Learning.- Chapter 3. Graph Neural Networks.- Chapter 4. Graph Neural Networks for Node Classification.- Chapter 5. The Expressive Power of Graph Neural Networks.- Chapter 6. Graph Neural Networks: Scalability.- Chapter 7. Interpretability in Graph Neural Networks.- Chapter 8. "Graph Neural Networks: Adversarial Robustness".- Chapter 9. Graph Neural Networks: Graph Classification.- Chapter 10. Graph Neural Networks: Link Prediction.- Chapter 11. Graph Neural Networks: Graph Generation.- Chapter 12. Graph Neural Networks: Graph Transformation.- Chapter 13. Graph Neural Networks: Graph Matching.- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks.- Chapter 16. Heterogeneous Graph Neural Networks.- Chapter 17. Graph Neural Network: AutoML.- Chapter 18. Graph Neural Networks: Self-supervised Learning.- Chapter 19. Graph Neural Network in Modern Recommender Systems.- Chapter 20. Graph Neural Network in Computer Vision.- Chapter 21. Graph Neural Networks in Natural Language Processing.- Chapter 22. Graph Neural Networks in Program Analysis.- Chapter 23. Graph Neural Networks in Software Mining.- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development".- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions".- Chapter 26. Graph Neural Networks in Anomaly Detection.- Chapter 27. Graph Neural Networks in Urban Intelligence.
£85.49
Elsevier Science & Technology Data Mining: Concepts and Techniques
Book SynopsisTable of Contents1. Introduction 2. Data, measurements, and data processing 3. Data warehousing and online analytical processing 4. Pattern mining: basic concepts and methods 5. Pattern mining: advanced methods 6. Classification: basic concepts and methods 7. Classification: advanced methods 8. Cluster analysis: basic concepts and methods 9. Cluster analysis: advanced methods 10. Deep learning 11. Outlier Detection 12. Data mining trends and research frontiers Appendix: Mathematical background
£65.50
Levine Querido The Boy from Clearwater: Book 1
Book SynopsisFreeman Book Award WinnerGLLI Translated YA Book Prize HonoreeAn incredible true story in graphic novel form that lays bare the tortured and triumphant history of Taiwan, an island claimed and fought over by many countries, through the life story of a man who lived through its most turbulent times.Part One: Taiwan, 1930s. Tsai Kun-lin, an ordinary boy born in Chingshui, recounts a carefree childhood despite the Japanese occupation: growing up happily with the company of nursery rhymes and picture books on Chingshui Street. As war emerges Tsai''s memories shift to military parades, air raids, and watching others face conscription into the army. It seems no one can escape. After the war, the book-loving teenager tries hard to learn Mandarin and believes he is finally stepping towards a comfortable future; but little does he know, a dark cloud awaits him ahead.Part Two: Taiwan, 1950s. In his second year at Taichung First Senior High School, Tsai is arrested simply for joining a book club and subsequently tortured, deprived of civil rights, and sent to Green Island for reformation. Lasting until his release in September 1960, Tsai, a victim of the White Terror era, spends ten years of his youth in prison on an unjust charge. But he is ready to embrace freedom.Experience parts one and two of the tour-de-force graphic novel series that took Taiwan by storm ? parts three and four coming May 2024. P R A I S E ? ?The stupendous achievements here are many: a synergistic literary success; a historic record of twentieth-century Taiwan; the remarkable account of a boy?s life; and a triumph of translation.??Booklist (starred) ?A story of persistence, both hopeful and chilling, and a powerful vision of the personal implications of Taiwan?s mid-century history.??School Library Journal ?Stunning.??Kirkus
£18.55
Springer Nature Switzerland AG Practical Gynecologic Pathology: Frequently Asked
Book SynopsisThis book serves as a quick review or summary of major diagnostic challenges in gynecologic pathology and embraces a Q/A format by providing the desired diagnostic knowledge. It combines the diagnostic approach as it relates to morphology and the relevant ancillary tests which impact on clinical outcome or management. Each topic is written by subspecialty gynecological pathologists in the field in making this a valuable book for a broad spectrum of readers, particularly pathology trainees, junior pathologists and general practicing pathologists. These authors provide concise and accurate answers for a variety of common, yet challenging diagnostic scenarios that are relevant and frequently encountered in daily practice. The book walks readers through current topics, controversies and diagnostic dilemmas for common gynecologic diseases with morphologic features and key ancillary tests, richly illustrated with figures and tables. To reflex the current advances in diagnostic gynecological pathology, the topics are closely correlated with the updated disease classifications and diagnostic criteria, particularly those recognized by the World Health Organization. Practical Gynecologic Pathology provides a concise summary and review for some of the most difficult, prevailing and emerging concepts in gynecologic pathology.Table of ContentsDiseases of the Vulva.- Diseases of the Vagina.- HPV-Associated Cervical Neoplasia.- NonHPV-Associated Cervical Neoplasia.- Uterine Epithelial Neoplasia.- Uterine Mesenchymal Neoplasia.- Gestational Trophoblastic Diseases.- Diseases of the Fallopian Tube.- Ovarian Epithelial Neoplasia.- Ovarian Germ cell and Sex Cord-Stromal Neoplasia.- Mescelineous Neoplasia of the Ovary.- Diseases of the Peritoneum.- Intraoperative Consultation.- Inherited Cancer Syndromes.- Immunohistochemistry.- Molecular Diagnosis.
£89.99
Springer Verlag, Singapore Graph Neural Networks: Foundations, Frontiers, and Applications
Book SynopsisDeep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.Table of ContentsChapter 1. Representation Learning.- Chapter 2. Graph Representation Learning.- Chapter 3. Graph Neural Networks.- Chapter 4. Graph Neural Networks for Node Classification.- Chapter 5. The Expressive Power of Graph Neural Networks.- Chapter 6. Graph Neural Networks: Scalability.- Chapter 7. Interpretability in Graph Neural Networks.- Chapter 8. "Graph Neural Networks: Adversarial Robustness".- Chapter 9. Graph Neural Networks: Graph Classification.- Chapter 10. Graph Neural Networks: Link Prediction.- Chapter 11. Graph Neural Networks: Graph Generation.- Chapter 12. Graph Neural Networks: Graph Transformation.- Chapter 13. Graph Neural Networks: Graph Matching.- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks.- Chapter 16. Heterogeneous Graph Neural Networks.- Chapter 17. Graph Neural Network: AutoML.- Chapter 18. Graph Neural Networks: Self-supervised Learning.- Chapter 19. Graph Neural Network in Modern Recommender Systems.- Chapter 20. Graph Neural Network in Computer Vision.- Chapter 21. Graph Neural Networks in Natural Language Processing.- Chapter 22. Graph Neural Networks in Program Analysis.- Chapter 23. Graph Neural Networks in Software Mining.- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development".- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions".- Chapter 26. Graph Neural Networks in Anomaly Detection.- Chapter 27. Graph Neural Networks in Urban Intelligence.
£999.99
Levine Querido The Boy from Clearwater: Book 1
Book Synopsis
£23.39
