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Introduction to Online Convex Optimization, second edition
New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process. In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular succes...
Legal Knowledge and Information Systems
Technological advances related to legal information, knowledge representation, engineering, and processing have aroused growing interest within the research community and the legal industry in recent years. These advances relate to areas such as computational and formal models of legal reasoning, legal data analytics, legal information retrieval, the application of machine learning techniques to different legal tasks, and the experimental evaluation of these systems. This book presents the proceedings of JURIX 2023, the 36th International Conference on Legal Knowledge and Information Systems, held from 18–20 December 2023 in Maastricht, the Netherlands. This annual conference has become re...
Large Language Models
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration...
War Narratives in Post-Conflict Societies
This book studies war narratives and their role in the political arenas of post-conflict societies, with a focus on the former Yugoslavia. How do politicians in postwar societies talk about the past war? How do they discursively represent vulnerable social groups created by the conflict? Does the nature of this representation depend on the politicians’ ideology, personal characteristics, or their record of combat service? The book answers these questions by pairing natural language processing tools and large corpora of parliamentary debates collected in three southeast European post-conflict societies (Bosnia-Herzegovina, Croatia, and Serbia). Using the latest advances in computer science,...
Integration and Innovation Orient to E-Society Volume 1
The IFIP series publishes state-of-the-art results in the sciences and technologies of information and communication Proceedings and post-proceedings of referred international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the IFIP series is to encourage education and the dissemination and exchange of information about all aspects of computing.
Artificial Intelligence Research and Development
Artificial intelligence has become an integral part of all our lives. Development is rapid in this exciting and far-reaching field, and keeping up to date with the latest research and innovation is crucial to all those working with the technology. This book presents the proceedings of the 24th edition of CCIA, the International Conference of the Catalan Association for Artificial Intelligence, held in Sitges, Spain, from 19 – 21 October 2022. This annual event serves as a meeting point not only for researchers in AI from the Catalan speaking territories (southern France, Catalonia, Valencia, the Balearic Islands and Alghero in Italy) but for researchers from around the world. The programme...
Learning Robot Vision under Insufficient Data
Machine learning is used today in a wide variety of applications, especially within computer vision, robotics, and autonomous systems. Example use cases include detecting people or other objects using cameras in autonomous vehicles, or navigating robots through collision-free paths to solve different tasks. The flexibility of machine learning is attractive as it can be applied to a wide variety of challenging tasks, without detailed prior knowledge of the problem domain. However, training machine learning models requires vast amounts of data, which leads to a significant manual effort, both for collecting the data and for annotating it. In this thesis, we study and develop methods for traini...
Low-Power Computer Vision
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
Molecular Advances in Diagnosis and Treatment of CNS Tumors
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Federated Learning for Future Intelligent Wireless Networks
Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how...
