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Graph Representation Learning
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 sy...
The Things We've Seen
Written in three parts, War Trilogy is a dazzling and anarchic exploration of social relations which offers thought-provoking ideas on our perceptions of humanity, history, violence, art and science. The first part follows a writer who travels to the small, uninhabited island of San Simon, where he witnesses events which impel him on a journey across several continents, chasing the phantoms of nameless people devastated by violence. The second book is narrated by Kurt, the fourth astronaut who secretly accompanied Armstrong, Aldrin and Collins on their mythical first voyage to the moon. Now living in Miami, an ageing Kurt revisits the important chapters of his life: from serving in the Vietnam War to his memory of seeing earth from space. In the third part, a woman embarks on a walking tour of the Normandy coast with the goal of re-enacting, step by step, the memory of another trip taken years before. On her journey along the rugged coastline, she comes across a number of locals, but also thousands of refugees newly arrived on Europe's shores, whose stories she follows on the TV in her lodgings.
Federated Learning
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated lear...
Fish Forum Book of abstracts 2018
This volume includes all the abstracts of the keynotes, oral contributions and posters presented by participants on the occasion of the Forum on Fisheries Science in the Mediterranean and the Black Sea (Fish Forum 2018). Organized by the GFCM at FAO headquarters, Rome, Italy, from 10 to 14 December 2018, in collaboration with technical partners, the Fish Forum 2018 is a first-of-the-kind event gathering scientists, researchers, engineers, academics, practitioners, managers and decision-makers from around the world to discuss and share knowledge on the latest developments in fisheries science. The material contained in this book of abstracts stems from the contributions received from particip...
Recursos humanos en investigación y desarrollo. Universidades y CSIC
Recursos humanos en investigación y desarrollo.--V.2.
Annual World Bank Conference on Development Economics 2010, Global
The Annual World Bank Conference on Development Economics (ABCDE) is one of the world s best-known series of conferences for the presentation and discussion of new knowledge on development. The conference provides a forum for the world s leading development thinkers to share new knowledge and ideas. 'Lessons from East Asia and the Global Financial Crisis' was the theme of the ABCDE held in Seoul, Republic of Korea, on June 22 24, 2009. The conference was co-organized by the Government of the Republic of Korea, the Korea Development Institute (KDI), and the World Bank.
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
