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Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
Book Description
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 synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
Book Description
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 synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: Zhiyuan Liu Publisher: Springer Nature ISBN: 9811555737 Category : Computers Languages : en Pages : 319
Book Description
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.
Author: Russell Tytler Publisher: Springer Science & Business Media ISBN: 9462092036 Category : Education Languages : en Pages : 213
Book Description
Constructing Representations to Learn in Science Current research into student learning in science has shifted attention from the traditional cognitivist perspectives of conceptual change to socio-cultural and semiotic perspectives that characterize learning in terms of induction into disciplinary literacy practices. This book builds on recent interest in the role of representations in learning to argue for a pedagogical practice based on students actively generating and exploring representations. The book describes a sustained inquiry in which the authors worked with primary and secondary teachers of science, on key topics identified as problematic in the research literature. Data from classroom video, teacher interviews and student artifacts were used to develop and validate a set of pedagogical principles and explore student learning and teacher change issues. The authors argue the theoretical and practical case for a representational focus. The pedagogical approach is illustrated and explored in terms of the role of representation to support quality student learning in science. Separate chapters address the implications of this perspective and practice for structuring sequences around different concepts, reasoning and inquiry in science, models and model based reasoning, the nature of concepts and learning, teacher change, and assessment. The authors argue that this representational focus leads to significantly enhanced student learning, and has the effect of offering new and productive perspectives and approaches for a number of contemporary strands of thinking in science education including conceptual change, inquiry, scientific literacy, and a focus on the epistemic nature of science.
Author: Maarten W. van Someren Publisher: Emerald Group Publishing ISBN: 9780080433431 Category : Computers Languages : en Pages : 360
Book Description
Aims to collect papers on learning declarative knowledge and problem solving skills that involve multiple representations such as graphical and mathematical representations, knowledge at different levels of abstraction. This book covers approaches to this topic from different perspectives: educational, cognitive modelling and machine learning.
Author: Avadhesh Kumar Publisher: CRC Press ISBN: 100048422X Category : Computers Languages : en Pages : 216
Book Description
A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which includes both basic and advanced concepts.
Author: Patricia Murphy Publisher: SAGE ISBN: 1446205703 Category : Education Languages : en Pages : 234
Book Description
Longstanding cultural heritages about the nature of knowledge continue to dominate Western education. Yet the ways of knowing represented through teaching and workplace practices, including assessment, and their relationship to views of learning, are often ignored in debates about learning. This book provides a rich collection of readings that challenge traditional understandings of knowledge and the view of mind that underpins them. It offers socioculturally informed alternatives and tools for innovating change and transforming practice that value different ways of knowing, embracing those that learners bring to educational and workplace settings. The book takes forward thinking about curriculum in a number of unique and important ways. It adopts a relational view of learning and knowledge, covers educational and workplace learning, and examines knowledge from a sociocultural perspective where learner identities are conceived as forms of competency or knoweldge. It presents challenging ways of thinking about knowledge and learning and considers how to enact these in practice. Drawing from the international literature, this book will be essential reading for students of curriculum, learning and assessment in all sectors from primary to further and higher education. It is suitable as a core text for masters and taught doctorate programmes. It will also be of interest to a wide range of professionals involved with the processes of curriculum, learning and the practice of teaching and assessment. It will be relevant to those in work-based and professional education and training and informal educationsl settings, as well as traditional educational institutions at all levels. A unique collection in a field that is underrepresented, it will also be of interest to an academic audience.
Author: Publisher: BRILL ISBN: 9087905289 Category : Education Languages : en Pages : 334
Book Description
Learning and teaching complex cultural knowledge calls for meaningful participation in different kinds of symbolic practices, which in turn are supported by a wide range of external representations, as gestures, oral language, graphic representations, writing and many other systems designed to account for properties and relations on some 2- or 3-dimensional objects.
Author: Ray Bareiss Publisher: Academic Press ISBN: 1483216373 Category : Computers Languages : en Pages : 184
Book Description
Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning covers the fundamental issues in cognitive science and the technology for solving real problems. This text contains six chapters and begins with a description of the rationale for the design of Protos Approach, its construction and performance. The succeeding chapters discuss how the Protos approach meets the requirements of representing concepts, using them for classification, and acquiring them from available training. These chapters also deal with the design and implementation of Protos. These topics are followed by a presentation of examples of the application of Protos to audiology and evaluate its performance. The final chapters survey related work in the areas of case-based reasoning and automated knowledge acquisition and the contributions of Protos approach. This book will be of great value to psychologists, psychiatrists, and researchers in the field of artificial intelligence.
Author: William L. William L. Hamilton
Author: William L. William L. Hamilton
Author: Zhiyuan Liu
Author: Andrea A. DiSessa
Author: Russell Tytler
Author: Maarten W. van Someren
Author: Avadhesh Kumar
Author: Katharina Morik
Author: Patricia Murphy
Author: 
Author: Ray Bareiss