Lifelong And Continual Learning Dialogue Systems
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| Author | : Sahisnu Mazumder |
| Publisher | : Springer Nature |
| Total Pages | : 180 |
| Release | : 2024-02-09 |
| Genre | : Computers |
| ISBN | : 3031481895 |
This book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments. The authors present the latest developments and techniques for building such continual learning dialogue systems. The book explains how these developments allow systems to continuously learn new language expressions, lexical and factual knowledge, and conversational skills through interactions and dialogues. Additionally, the book covers techniques to acquire new training examples for learning new tasks during the conversation. The book also reviews existing work on lifelong learning and discusses areas for future research.
| Author | : Zhiyuan Sun |
| Publisher | : Springer Nature |
| Total Pages | : 187 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031015819 |
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.
| Author | : Xin Wang |
| Publisher | : Springer Nature |
| Total Pages | : 780 |
| Release | : 2023-04-13 |
| Genre | : Computers |
| ISBN | : 3031306783 |
The four-volume set LNCS 13943, 13944, 13945 and 13946 constitutes the proceedings of the 28th International Conference on Database Systems for Advanced Applications, DASFAA 2023, held in April 2023 in Tianjin, China. The total of 125 full papers, along with 66 short papers, are presented together in this four-volume set was carefully reviewed and selected from 652 submissions. Additionally, 15 industrial papers, 15 demo papers and 4 PhD consortium papers are included. The conference presents papers on subjects such as model, graph, learning, performance, knowledge, time, recommendation, representation, attention, prediction, and network.
| Author | : Erik Marchi |
| Publisher | : Springer Nature |
| Total Pages | : 453 |
| Release | : 2021-03-10 |
| Genre | : Technology & Engineering |
| ISBN | : 981159323X |
This book compiles and presents a synopsis on current global research efforts to push forward the state of the art in dialogue technologies, including advances to language and context understanding, and dialogue management, as well as human–robot interaction, conversational agents, question answering and lifelong learning for dialogue systems.
| Author | : Athanasios Tsanas |
| Publisher | : Springer Nature |
| Total Pages | : 701 |
| Release | : 2023-06-10 |
| Genre | : Medical |
| ISBN | : 303134586X |
This book constitutes the refereed proceedings of the 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2022, which took place in Thessaloniki, Greece, in December 2022. The 45 full papers included in this volume were carefully reviewed and selected from 120 submissions. The papers are organized in the following topical sections: personal informatics and wearable devices; computer vision; IoT-HR: Internet of things in health research; pervasive health for COVID-19; machine learning, human activity recognition and speech recognition; software frameworks and interoperability; facial recognition, gesture recognition and object detection; machine learning, predictive models and personalised healthcare; human-centred design of pervasive health solutions; personalized healthcare.
| Author | : Albert Bifet |
| Publisher | : Springer Nature |
| Total Pages | : 512 |
| Release | : |
| Genre | : |
| ISBN | : 3031703626 |
| Author | : Zhiyuan Chen |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 209 |
| Release | : 2018-08-14 |
| Genre | : Computers |
| ISBN | : 168173303X |
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.
| Author | : Elmar Nöth |
| Publisher | : Springer Nature |
| Total Pages | : 318 |
| Release | : |
| Genre | : |
| ISBN | : 3031705637 |
| Author | : Parikshit N Mahalle |
| Publisher | : CRC Press |
| Total Pages | : 309 |
| Release | : 2024-06-06 |
| Genre | : Computers |
| ISBN | : 1040031137 |
This book explores the need for a data‐centric AI approach and its application in the multidisciplinary domain, compared to a model‐centric approach. It examines the methodologies for data‐centric approaches, the use of data‐centric approaches in different domains, the need for edge AI and how it differs from cloud‐based AI. It discusses the new category of AI technology, "data‐centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‐centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. • Includes a collection of case studies with experimentation results to adhere to the practical approaches • Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways • Discusses methodologies to achieve accurate results by improving the quality of data • Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications
| Author | : Joanne R. Duffy, PhD, RN, FAAN |
| Publisher | : Springer Publishing Company |
| Total Pages | : 377 |
| Release | : 2022-12-22 |
| Genre | : Medical |
| ISBN | : 0826136966 |
Praise for Previous Editions: "I enjoyed the book. It was well written, current and timely with changes in the healthcare system. The reflective questions and practice analysis were great and would be wonderful to use with students at the graduate and undergraduate levels." –Doody's Medical Reviews Freshly updated, this acclaimed text demonstrates how nurses can promote caring relationships with individuals, groups, and communities in various health care settings to ensure better patient outcomes, lower costs, and greater clinician well-being. The book is grounded in the author's Quality Caring Model©, a middle range theory that analyzes relationships among the self, the community, patients and families, and the health care team. It expands upon the concept of self-caring and examines current thinking on employee work engagement and creating value. Interviews with practicing nurses who describe current healthcare challenges and strategies for managing them also enrich the text. Written for nursing students, clinicians, educators, and leaders, the book delves into the intricacies of relational healthcare and imparts strategies to ameliorate the ills of our current health system by focusing on nursing care that advances equity, pursues innovative and advanced educational experiences, leads, and engages in practice across multiple settings. Chapters apply the model to patients and families and provide optimal learning strategies to facilitate quality-caring competencies. Woven throughout the text are case studies, interviews, exemplars, and relevant lessons to put theory into practice. An Instructor's Manual includes a crosswalk of QCM concepts, core competencies, and performance standards; student assignments, reflections, and value exercises; and PowerPoints. New to the Fourth Edition: Instructor resources and power point slides Updates to address latest recommendations from NAM's The Future of Nursing 2020-2030, ANA's 2021 Nursing Scope and Standards of Practice, AACN's 2021 The Essentials, and AACN's 2021 Entry-to-Practice Nurse Residency Program Standards Expanded content on the challenges of self-caring with practical guidance for preventing moral injury Examples of caring behaviors in action Current thinking on employee work engagement and creating value Interviews with practicing nurses reflecting challenges and strategies for dealing with current state of healthcare Updated information on resiliency, long-term career planning, and work engagement Revised educational and leadership strategies to address the post pandemic health system Key Features: Examines in depth the evolution, key concepts, and clinical, educational, and leadership applications of the Quality Caring Model Underscores the significance of caring relationships in improving the safety and quality of healthcare systems Delivers comprehensive, concise, evidence-based content throughout Offers practical insights with real-life case studies and interviews in diverse community and academic settings Includes memorable quotes, learning objectives, boxed calls to action, key summary points, reflective exercises, and Practice Analysis supporting an active, learner-centered approach
