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Automated Machine Learning
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Automated Machine Learning and Meta-Learning for Multimedia
This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.
Learning and Intelligent Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011. The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.
Computational Models of Argument
Argumentation, which has long been a topic of study in philosophy, has become a well-established aspect of computing science in the last 20 years. This book presents the proceedings of the fifth conference on Computational Models of Argument (COMMA), held in Pitlochry, Scotland in September 2014. Work on argumentation is broad, but the COMMA community is distinguished by virtue of its focus on the computational and mathematical aspects of the subject. This focus aims to ensure that methods are sound – that they identify arguments that are correct in some sense – and provide an unambiguous specification for implementation; producing programs that reason in the correct way and building systems capable of natural argument or of recognizing argument. The book contains 24 long papers and 18 short papers, and the 21 demonstrations presented at the conference are represented in the proceedings either by an extended abstract or by association with another paper. The book will be of interest to all those whose work involves argumentation as it relates to artificial intelligence.
Bayesian Optimization
A comprehensive introduction to Bayesian optimization that starts from scratch and carefully develops all the key ideas along the way.
Deep Reinforcement Learning
Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how sub...
Learning and Intelligent Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Learning and Optimization, LION 8, which was held in Gainesville, FL, USA, in February 2014. The 33 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as algorithm configuration; multiobjective optimization; metaheuristics; graphs and networks; logistics and transportation; and biomedical applications.
Designing Deep Learning Systems
A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software...
ECAI 2023
Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrat...
Handbook of Satisfiability
Propositional logic has been recognized throughout the centuries as one of the cornerstones of reasoning in philosophy and mathematics. Over time, its formalization into Boolean algebra was accompanied by the recognition that a wide range of combinatorial problems can be expressed as propositional satisfiability (SAT) problems. Because of this dual role, SAT developed into a mature, multi-faceted scientific discipline, and from the earliest days of computing a search was underway to discover how to solve SAT problems in an automated fashion. This book, the Handbook of Satisfiability, is the second, updated and revised edition of the book first published in 2009 under the same name. The handb...
