Reclaiming Representation
Reclaiming Representation

Author: Monica Brito Vieira

Publisher: Taylor & Francis

Published: 2017-05-25

Total Pages: 245

ISBN-13: 1317400941

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Representation is integral to the functioning and legitimacy of modern government. Yet political theorists have often been reluctant to engage directly with questions of representation, and empirical political scientists have closed down such questions by making representation synonymous with congruence. Conceptually unproblematic and normatively inert for some, representation has been deemed impossible to pin down analytically and to defend normatively by others. But this is changing. Political theorists are now turning to political representation as a subject worthy of theoretical investigation in its own right. In their effort to rework the theory of political representation, they are also hoping to impact how representation is assessed and studied empirically. This volume gathers together chapters by key contributors to what amounts to a "representative turn" in political theory. Their approaches and emphases are diverse, but taken together they represent a compelling and original attempt at re-conceptualizing political representation and critically assessing the main theoretical and political implications following from this, namely for how we conceive and assess representative democracy. Each contributor is invited to look back and ahead on the transformations to democratic self-government introduced by the theory and practice of political representation. Representation and democracy: outright conflict, uneasy cohabitation, or reciprocal constitutiveness? For those who think democracy would be better without representation, this volume is a must-read: it will question their assumptions, while also exploring some of the reasons for their discomfort. Reclaiming Representation is essential reading for scholars and graduate researchers committed to staying on top of new developments in the field.

Representation of Lie Groups and Special Functions
Representation of Lie Groups and Special Functions

Author: N.Ja. Vilenkin

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 518

ISBN-13: 9401728852

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In 1991-1993 our three-volume book "Representation of Lie Groups and Spe cial Functions" was published. When we started to write that book (in 1983), editors of "Kluwer Academic Publishers" expressed their wish for the book to be of encyclopaedic type on the subject. Interrelations between representations of Lie groups and special functions are very wide. This width can be explained by existence of different types of Lie groups and by richness of the theory of their rep resentations. This is why the book, mentioned above, spread to three big volumes. Influence of representations of Lie groups and Lie algebras upon the theory of special functions is lasting. This theory is developing further and methods of the representation theory are of great importance in this development. When the book "Representation of Lie Groups and Special Functions" ,vol. 1-3, was under preparation, new directions of the theory of special functions, connected with group representations, appeared. New important results were discovered in the traditional directions. This impelled us to write a continuation of our three-volume book on relationship between representations and special functions. The result of our further work is the present book. The three-volume book, published before, was devoted mainly to studying classical special functions and orthogonal polynomials by means of matrix elements, Clebsch-Gordan and Racah coefficients of group representations and to generaliza tions of classical special functions that were dictated by matrix elements of repre sentations.

Graph Representation Learning
Graph Representation Learning

Author: William L. William L. Hamilton

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 141

ISBN-13: 3031015886

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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.

Representation Learning for Natural Language Processing
Representation Learning for Natural Language Processing

Author: Zhiyuan Liu

Publisher: Springer Nature

Published: 2020-07-03

Total Pages: 319

ISBN-13: 9811555737

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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.

Advances in Analysis
Advances in Analysis

Author: Charles Fefferman

Publisher: Princeton University Press

Published: 2014-01-05

Total Pages: 478

ISBN-13: 0691159416

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Princeton University's Elias Stein was the first mathematician to see the profound interconnections that tie classical Fourier analysis to several complex variables and representation theory. His fundamental contributions include the Kunze-Stein phenomenon, the construction of new representations, the Stein interpolation theorem, the idea of a restriction theorem for the Fourier transform, and the theory of Hp Spaces in several variables. Through his great discoveries, through books that have set the highest standard for mathematical exposition, and through his influence on his many collaborators and students, Stein has changed mathematics. Drawing inspiration from Stein’s contributions to harmonic analysis and related topics, this volume gathers papers from internationally renowned mathematicians, many of whom have been Stein’s students. The book also includes expository papers on Stein’s work and its influence. The contributors are Jean Bourgain, Luis Caffarelli, Michael Christ, Guy David, Charles Fefferman, Alexandru D. Ionescu, David Jerison, Carlos Kenig, Sergiu Klainerman, Loredana Lanzani, Sanghyuk Lee, Lionel Levine, Akos Magyar, Detlef Müller, Camil Muscalu, Alexander Nagel, D. H. Phong, Malabika Pramanik, Andrew S. Raich, Fulvio Ricci, Keith M. Rogers, Andreas Seeger, Scott Sheffield, Luis Silvestre, Christopher D. Sogge, Jacob Sturm, Terence Tao, Christoph Thiele, Stephen Wainger, and Steven Zelditch.

Embeddings in Natural Language Processing
Embeddings in Natural Language Processing

Author: Mohammad Taher Pilehvar

Publisher: Morgan & Claypool Publishers

Published: 2020-11-13

Total Pages: 177

ISBN-13: 1636390226

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Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.

Unbounded Operator Algebras and Representation Theory
Unbounded Operator Algebras and Representation Theory

Author: K. Schmüdgen

Publisher: Birkhäuser

Published: 2013-11-11

Total Pages: 381

ISBN-13: 3034874693

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*-algebras of unbounded operators in Hilbert space, or more generally algebraic systems of unbounded operators, occur in a natural way in unitary representation theory of Lie groups and in the Wightman formulation of quantum field theory. In representation theory they appear as the images of the associated representations of the Lie algebras or of the enveloping algebras on the Garding domain and in quantum field theory they occur as the vector space of field operators or the *-algebra generated by them. Some of the basic tools for the general theory were first introduced and used in these fields. For instance, the notion of the weak (bounded) commutant which plays a fundamental role in thegeneraltheory had already appeared in quantum field theory early in the six ties. Nevertheless, a systematic study of unbounded operator algebras began only at the beginning of the seventies. It was initiated by (in alphabetic order) BORCHERS, LASSNER, POWERS, UHLMANN and VASILIEV. J1'rom the very beginning, and still today, represen tation theory of Lie groups and Lie algebras and quantum field theory have been primary sources of motivation and also of examples. However, the general theory of unbounded operator algebras has also had points of contact with several other disciplines. In particu lar, the theory of locally convex spaces, the theory of von Neumann algebras, distri bution theory, single operator theory, the momcnt problem and its non-commutative generalizations and noncommutative probability theory, all have interacted with our subject.

Learning with Multiple Representations
Learning with Multiple Representations

Author: Maarten W. van Someren

Publisher: Emerald Group Publishing

Published: 1998

Total Pages: 360

ISBN-13: 9780080433431

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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.

Advances In Algebra, Proceedings Of The Icm Satellite Conference In Algebra And Related Topics
Advances In Algebra, Proceedings Of The Icm Satellite Conference In Algebra And Related Topics

Author: Kar Ping Shum

Publisher: World Scientific

Published: 2003-07-07

Total Pages: 531

ISBN-13: 9814486787

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This is the proceedings of the ICM2002 Satellite Conference on Algebras. Over 175 participants attended the meeting. The opening ceremony included an address by R Gonchidorazh, former vice-president of the Mongolian Republic in Ulaanbaatar. The topics covered at the conference included general algebras, semigroups, groups, rings, hopf algebras, modules, codes, languages, automation theory, graphs, fuzzy algebras and applications.